**footnotetext: Equal contribution

K Ram<EMAIL_ADDRESS>Vishal<EMAIL_ADDRESS>Nihar Ranjan<EMAIL_ADDRESS>R. Venkatesh
<EMAIL_ADDRESS>Video Analytics Lab,
Department of Computational and Data Sciences,
Indian Institute of Science,
Bangalore, India FSDA for Low Light RAW Enhancement

# Few-Shot Domain Adaptation for Low Light RAW Image Enhancement

###### Abstract

Enhancing practical low light raw images is a difficult task due to severe
noise and color distortions from short exposure time and limited illumination.
Despite the success of existing Convolutional Neural Network (CNN) based
methods, their performance is not adaptable to different camera domains. In
addition, such methods also require large datasets with short-exposure and
corresponding long-exposure ground truth raw images for each camera domain,
which is tedious to compile. To address this issue, we present a novel few-
shot domain adaptation method to utilize the existing source camera labeled
data with few labeled samples from the target camera to improve the target
domain’s enhancement quality in extreme low-light imaging. Our experiments
show that only ten or fewer labeled samples from the target camera domain are
sufficient to achieve similar or better enhancement performance than training
a model with a large labeled target camera dataset. To support research in
this direction, we also present a new low-light raw image dataset captured
with a Nikon camera, comprising short-exposure and their corresponding long-
exposure ground truth images. The code is available at
https://val.cds.iisc.ac.in/HDR/BMVC21/index.html.

##  1 Introduction

Capturing high-quality photos in low illumination is a fundamental yet
challenging task. Increasing the ISO improves visibility; however, it also
increases sensor noise. Longer exposure times improve the image but require a
tripod to avoid camera motion and motion blur. Methods like enabling flash or
image editing also present their own challenges. Low exposure image
enhancement helps generate low-light scenes as if they were captured with a
longer exposure time. It enables fast low illumination photography without a
tripod. As shown in Fig. 1(a), the images captured in such settings possess a
high degree of noise and color distortion. Existing single image denoising
methods [Dabov et al.(2007)Dabov, Foi, Katkovnik, and Egiazarian, Plotz and
Roth(2017)] perform poorly and fail to correct color distortions in low light.

An alternative is to merge a burst of short exposure images to reduce noise
[Hasinoff et al.(2016)Hasinoff, Sharlet, Geiss, Adams, Barron, Kainz, Chen,
and Levoy, Liu et al.(2014)Liu, Yuan, Tang, Uyttendaele, and Sun, Mildenhall
et al.(2018)Mildenhall, Barron, Chen, Sharlet, Ng, and Carroll]. However, the
burst images must be aligned for the camera and object motion, and aligning
them in low-light conditions is a challenge. LSID, [Chen et al.(2018a)Chen,
Chen, Xu, and Koltun] a recent deep learning based method enhances raw low-
light images by performing denoising, color improvement, and demosaicing, all
with a single lightweight model. Despite the success of recent methods, there
are two persisting challenges:

Figure 1: Qualitative comparison between different methods with an input to
the model from the Canon dataset (left-most). The models are: LSID trained on
(a) full 161 Sony source images, (b) only 6 Canon images, and (c) full Canon
dataset. (d) 6 Canon images and 161 Sony images with proposed few-shot domain
adaptation method (discussed in section 3).

Domain Shift: CNNs are heavily data-sensitive; Since raw images captured with
cameras from different manufacturers exhibit variations in color-space and
noise characteristics, a model trained with one camera’s raw data performs
sub-optimally on another camera’s raw data (see Table 2). Hence, there exists
a domain shift across different camera raw domains. In this work, we consider
each camera as a separate domain. As seen from the Fig. 1, cross-camera domain
performance is poor as there are color distortions (green patches on the wall)
and loss of finer details (missing cat’s whiskers) due to the shift across
camera domains.

Tedious to collect labeled data: Collecting a large dataset of short-exposure
and long-exposure raw image pairs for each camera is a difficult task. There
must be no object motion, and to avoid camera misalignment, a tripod is
necessary to capture long-exposure images (typically 10 seconds or more).
Further, a smartphone or an IR remote is required to trigger the camera to
avoid camera shake arising from physically pressing the camera click button.
Thus, capturing a large-scale labeled dataset for different cameras is an
arduous task.

To address the above mentioned challenges, we propose a paradigm shift for the
low-light raw image enhancement task using few-shot learning and domain
adaptation. We use a large collection of existing source camera labeled data
to improve the performance and generate the output in the target domain by
transferring the task onto a new target camera dataset with only a few labeled
samples. In summary, our contributions are as follows:

  1. (i)

To the best of our knowledge, we propose the first few-shot domain adaptation
method for low-light raw image enhancement.

  2. (ii)

We show that, with less than ten labeled samples from the target domain, our
approach can outperform a model trained with a complete target domain dataset.

  3. (iii)

We present experiments and ablations to illustrate the effectiveness of our
method.

  4. (iv)

We present a new Nikon camera dataset with short-exposure and long-exposure
ground truth raw image pairs for the benefit of the research community.

##  2 Related Work

Figure 2: First-order and second order statistics for an image captured with
Nikon and Canon cameras. (a) Histogram of intensities, (b) Histogram of
spatial derivatives of intensity, (c) Joint histogram of responses from
convolution filters.

Table 1: Comparing LSID models trained with a complete source camera dataset
and tested on respective target camera datasets.

Testing ($\rightarrow$) | Sony | Nikon | Canon
---|---|---|---
Training ($\downarrow$) | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM
Sony [Chen et al.(2018a)Chen, Chen, Xu, and Koltun] | 28.50 | 0.774 | 25.90 | 0.693 | 27.41 | 0.845
Nikon | 19.95 | 0.481 | 30.74 | 0.803 | 24.34 | 0.767
Canon [Can()] | 18.51 | 0.542 | 23.27 | 0.847 | 32.32 | 0.899

Table 2: Details of the datasets used in our work.

Datasets | Exposure | Training | Testing
---|---|---|---
Ratios | Images | Images
Sony [Chen et al.(2018a)Chen, Chen, Xu, and Koltun] | 90,15,300 | 161 | 36
Nikon | 100,300 | 53 | 24
Canon [Can()] | 50,150,300 | 44 | 21

Low-light Image Enhancement: Existing low-light image enhancement methods
require paired low/well-lit image scenes in RGB space and assume the images to
be captured with minimal noise. While such methods capture global information
suitably, their performance in extreme low-light conditions is sub-par. The
histogram equalization method is useful for increasing the dynamic range in a
global context and is sub-optimal for extreme low-light enhancement [Chen et
al.(2018b)Chen, Chen, Chao, and Yang]. Retinex [Park et al.(2017)Park, Yu,
Moon, Ko, and Paik] methods assume that the images have sufficient information
to map the reflectance and enhance the low-light image. Similarly, [Wang et
al.(2019)Wang, Zhang, Fu, Shen, Zheng, and Jia] formulates the illumination
estimation for enhancing underexposed images akin to expert retouched ground
truth without image-to-image regression. Methods such as EnlightenGAN [Jiang
et al.(2021)Jiang, Gong, Liu, Cheng, Fang, Shen, Yang, Zhou, and Wang], a
generative model with an attention U-Net [Ronneberger et al.(2015)Ronneberger,
Fischer, and Brox], [Xu et al.(2020)Xu, Yang, Yin, and Lau] for decomposition
and enhancement and MIRNet [Zamir et al.(2020)Zamir, Arora, Khan, Hayat, Khan,
Yang, and Shao] for attention aggregation have performed well in sRGB low-
light enhancement.

Single Image Denoising: Non-deep methods for single image denoising either use
engineered features [Simoncelli and Adelson(1996), Rudin et al.(1992)Rudin,
Osher, and Fatemi], or assume the noise model to be uniform and additive. Such
assumptions in parametric methods are unsuitable for real-world low-light
enhancement. Non-parametric methods depend on sparse image priors such as
smoothness and self-similarity [Gu et al.(2014)Gu, Zhang, Zuo, and Feng,
Mairal et al.(2009)Mairal, Bach, Ponce, Sapiro, and Zisserman, Łoza et
al.(2013)Łoza, Bull, Hill, and Achim, Dabov et al.(2007)Dabov, Foi, Katkovnik,
and Egiazarian] and are more expressive than parametric methods. BM3D [Dabov
et al.(2007)Dabov, Foi, Katkovnik, and Egiazarian, Plotz and Roth(2017)], a
non-blind denoising method requiring noise and color model information, has
outperformed deep methods in accuracy and noise robustness; however, it is
prone to over smoothing in low light conditions. Several deep methods
leveraging CNN based advancements have been proposed for denoising [Zhang et
al.(2017)Zhang, Zuo, Chen, Meng, and Zhang, Jain and Seung(2008), Xie et
al.(2012)Xie, Xu, and Chen, Zhang et al.(2018)Zhang, Zuo, and Zhang, Ulyanov
et al.(2018)Ulyanov, Vedaldi, and Lempitsky]. Autoencoders [Xie et
al.(2012)Xie, Xu, and Chen, Lore et al.(2017)Lore, Akintayo, and Sarkar] and
MLPs [Burger et al.(2012)Burger, Schuler, and Harmeling] have shown sub-par
performance on real-world raw sensor noise. Unprocess [Brooks et
al.(2019)Brooks, Mildenhall, Xue, Chen, Sharlet, and Barron] learns the
denoising pipeline and relevant photometric parameters by ‘unprocessing’ the
image and is different from our goal to learn the camera’s color and noise
model effectively.

Few-shot Domain Adaptation: Few-shot learning [Sung et al.(2018)Sung, Yang,
Zhang, Xiang, Torr, and Hospedales, Finn et al.(2017)Finn, Abbeel, and Levine,
Sun et al.(2019)Sun, Liu, Chua, and Schiele, Prabhakar et al.(2021)Prabhakar,
Senthil, Agrawal, Babu, and Gorthi] and Domain adaptation [Bousmalis et
al.(2017)Bousmalis, Silberman, Dohan, Erhan, and Krishnan, Rozantsev et
al.(2018)Rozantsev, Salzmann, and Fua] techniques are well explored in the
context of many computer vision tasks. Several few-shot domain adaptation
works [Sahoo et al.(2018)Sahoo, Le, Liu, and Hoi, Motiian et al.(2017)Motiian,
Jones, Iranmanesh, and Doretto] use few labeled samples with many unlabeled
samples in the target domain for image classification. Similarly, DA-FSL [Zhao
et al.(2021)Zhao, Ding, Lu, Xiang, Niu, Guan, and Wen] is a few-shot domain
adaptive prototypical learning method for recognition. The meta-learning
paradigm [Casas et al.(2019)Casas, Klimmek, Carneiro, Navab, and Belagiannis]
has also shown great promise in image denoising but depends on prior noise
models to partially represent real noise. To the best of our knowledge, there
has been no prior investigation of few-shot DA in inverse-imaging for raw
camera domains.

As noted in [Brooks et al.(2019)Brooks, Mildenhall, Xue, Chen, Sharlet, and
Barron], different camera sensors exhibit different noise models, and the
process of capturing short-exposure and corresponding long-exposure raw images
in low-light conditions is expensive and time-consuming. While [Wang et
al.(2020)Wang, Huang, Xu, Liu, Liu, and Wang] has proposed efficient low-light
enhancement, it is only for one type of smartphone camera. As a step toward
tackling these challenges, we introduce the first of its kind few-shot domain
adaptation and enhancement method for low-light conditions in the raw domain
that is lightweight and highly effective.

##  3 Proposed method

|
---|---
(a) | (b)
Figure 3: (a) Example short-exposure and long-exposure image pairs from the
Nikon dataset. The short exposure images are almost entirely dark whereas the
long-exposure images have immense scene information. (b) Overview of our few-
shot domain adaptation method.

With a noisy raw image captured with low-exposure time (i.e., shutter speed)
as input, our CNN-based approach is trained to predict a clean long-exposure
sRGB output of the same scene. The input is multiplied by an exposure factor
calculated by the ratio of output and input exposure times. For example, to
generate a 10-second long exposure output, the input 0.1-second low exposure
image must be multiplied by 100. As a result of this operation, along with
illumination, the noise is also amplified proportionally. Since we multiply
the factor in the unprocessed raw domain and expect the output in the sRGB
domain, the network must learn camera hardware-specific enhancement as well as
its entire ISP pipeline (lens correction, demosaicing, white balancing, color
manipulation, tone curve application, color space transform, and Gamma
correction). Thus, a model trained on one specific camera data (source domain)
does not translate similar performance to a different camera (target domain),
hence the domain gap. In this paper, we propose to transfer the enhancement
task from large labeled source data and generate output in the target domain
using few labeled target data.

Problem formulation: We denote source domain ($\mathbf{S}$) with input short-
exposure images as $\\{S_{n}\\}$ and corresponding long-exposure ground truth
as $\widehat{\mathbf{S}}\\!=\\!\widehat{S}_{n}$, $\forall n=1,\cdots,N$.
Similarly, the target domain ($\mathbf{T}$) consists of input images
$\\{T_{m}\\}$ and corresponding ground truth,
$\widehat{\mathbf{T}}\\!=\\!\widehat{T}_{m}$, $\forall m=1,\cdots,M$. Note
that $N$ is much greater than $M$, $N\gg M$. With both $\mathbf{S}$ and
$\mathbf{T}$ as input, we train a CNN model ($\mathbb{N}$) to generate
enhanced long-exposure output ($\widetilde{\mathbf{S}}$ and
$\widetilde{\mathbf{T}}$). Our method is illustrated in Fig. 3(b) with the
source and target training pipelines. It is an end-to-end trainable deep
network that takes the raw sensor arrays as input and performs image
enhancement utilizing the source data for few-shot domain adaptation to the
target data.

Encoders: The significant domain gap between the source and target domains
necessitates the extraction of separate and independent features from each
domain before processing with a shared enhancement network ($\mathbb{N}$).
Hence, we use a source encoder ($\mathcal{E}_{S}$) and a target encoder
($\mathcal{E}_{T}$). We first pack the input raw sensor arrays into a four-
channel vector (for Bayer arrays from Sony, Nikon, and Canon cameras) and
subtract the black level (reference voltage). Then, the packed array is
multiplied by the exposure ratio factor and passed as input to the respective
domain encoder. It should be noted that the exposure ratio factors need not be
the same between the source and the target domain (See Table 2). For the
encoder network, we use three convolutional layers with $\\{16,32,64\\}$
filters and 3$\times$3 kernel size.

Enhancement Network: The source and target domain encoder features are passed
separately to a shared common enhancement network, $\mathbb{N}$. By having a
common enhancement network, the large pool of source data helps to improve the
enhancement quality of $\mathbb{N}$, while the few target samples ensure that
the output is in the target domain. We use U-Net architecture for the
enhancement network. Further, the network has a pixel shuffle layer to convert
12-channel prediction to 16-bit three channel sRGB output. The objective of
$\mathbb{N}$ is to enhance, denoise, perform other ISP operations (AWB, color
manipulation, etc.), and finally demosaicking to generate an sRGB output.
$\mathbb{N}$ generates enhanced output $\widetilde{\mathbf{T}}$ for the target
domain data as,
$\widetilde{\mathbf{T}}=\mathbb{N}\big{(}\mathcal{E}_{T}(\mathbf{T})\big{)}$.
Similarly, $\widetilde{\mathbf{S}}$ for the source domain as,
$\widetilde{\mathbf{S}}=\mathbb{N}\big{(}\mathcal{E}_{S}(\mathbf{S})\big{)}$.

Losses: For the target domain, we compute the $\ell_{1}$ loss between the
prediction ($\widetilde{\mathbf{T}}$) and the ground truth
($\widehat{\mathbf{T}}$) as,
$\mathcal{L}_{target}=\ell_{1}\big{(}\widetilde{\mathbf{T}},\widehat{\mathbf{T}}\big{)}$.
The source domain loss consists of two components: cosine similarity loss and
SSIM loss. We compute cosine similarity between $\widetilde{\mathbf{S}}$ and
$\widehat{\mathbf{S}}$ as,
$\mathcal{L}_{CS}(\widetilde{\mathbf{S}},\widehat{\mathbf{S}})=1-\frac{{\widetilde{\mathbf{S}}\cdotp\widehat{\mathbf{S}}}}{\|\widetilde{\mathbf{S}}\|\times\|\widehat{\mathbf{S}}\|}$.
Cosine similarity loss is weak supervision for the source domain and is used
instead of $\ell_{1}$ loss since $N\gg M$, and using a strong supervision loss
like $\ell_{1}$ optimizes for pixel values to train $\mathbb{N}$, making the
network predict the output in the source domain even for target domain input.
Cosine similarity loss ensures that the prediction and the ground truth are in
a similar direction. Hence with $\mathcal{L}_{CS}$, $\mathbb{N}$ can still
perform enhancement while predicting in target domain even for source domain
input. Further, when trained with Sony as source and 4-shot Nikon as target
(Table 7) with $L_{1}$ loss for the source, we obtain only 27.14dB PSNR for
target domain validation, whereas using $\mathcal{L}_{CS}$ loss for source
achieves 30.30dB PSNR.

Figure 4: Qualitative comparison with methods tested on Nikon (top row) and
Canon (bottom row) target images. (a) Input after multiplying by exposure
factor, results from (b) HDRCNN and (c) Unprocess methods are after training
on full Sony source and fine-tuning on $k$-shot target images, LSID with (d)
$k$-shot target images and (e) full target training dataset ($k$=53 for Nikon
and $k$=44 for Canon), (f) Proposed few-shot domain adaptation method with 161
Sony source images and 4-shot Nikon (top row) and 6-Canon (bottom row) target
images.

From experiments (in section 5), we find better enhancement (in terms of PSNR)
using the structural similarity index measure (SSIM) [Wang et al.(2004)Wang,
Bovik, Sheikh, and Simoncelli] to compute perceived degradation and preserve
the spatial structure in the source output with respect to the ground truth.
We do not use SSIM directly on the 16-bit data as that causes the source data
to heavily influence the domain adaptation since the source dataset is much
larger. Hence, we apply SSIM in JPEG compressed 8-bit domain, where the
structural domain difference is less. Since type-casting the 16-bit data to
8-bit will still possess domain-specific details, we train a 16-to-8-bit U-net
model ($\mathcal{D}$ in Fig. 3) to convert the output from 16-bit to post-
processed 8-bit representation.

The $\mathcal{D}$ network is trained to perform the following non-linear
operations: White balancing, Gamma correction, Quantization, and JPEG
compression. Even after JPEG compression, the prediction may have traces of
source domain specific color information. Further, the SSIM loss is a strong
pixel-wise supervision, and in order to avoid the source domain from heavily
influencing $\mathbb{N}$, we compute SSIM loss only in grayscale space, not in
RGB color space. Also, it follows the intuition that the structure and edge
information of a scene will remain the same across images captured with
different cameras, while the color space representation may vary. We find that
without SSIM loss for the source, we obtain 29.38dB PSNR on target domain
validation, whereas using SSIM loss achieves 30.30dB PSNR (Table 7). For
computing the SSIM loss, the ground truth ($\widehat{\mathbf{S}}$) is also
converted offline to post-processed 8-bit data ($\widehat{\mathbf{S}}_{PP}$)
using the rawpy post process function. Hence, the loss is obtained by
computing SSIM loss between $\mathcal{D}(\widetilde{\mathbf{S}})$ and
$\widehat{\mathbf{S}}_{PP}$,
$\mathcal{L}_{SSIM}=1-SSIM\Big{(}\mathcal{D}(\widetilde{\mathbf{S}}),\widehat{\mathbf{S}}_{PP}\Big{)}$.
In Fig. 3, the top branch guided by the deep red arrows shows the entire
source camera training pipeline. It should be noted that $\mathcal{D}$ is used
only to compute the loss but not in inference. Finally, we use the sum of
cosine similarity loss ($\mathcal{L}_{CS}$) as well as the SSIM loss
calculated in the 8-bit domain as the total loss for the source camera
pipeline: $\mathcal{L}_{source}=\mathcal{L}_{CS}+\mathcal{L}_{SSIM}$. The
total loss is the sum of target and source domain losses:
$\mathcal{L}_{total}=\mathcal{L}_{target}+\mathcal{L}_{source}$.

##  4 Experiments

Datasets: We use the Sony camera dataset [Chen et al.(2018a)Chen, Chen, Xu,
and Koltun] for our source training pipeline. We expect the diverse, high-
quality low-light scenes from this dataset to aid few-shot domain adaptation
performance in terms of the color spaces and noise model learned by our
method.

Table 3: Quantitative comparison of Sony as source with Nikon or Canon as
target. The improvement of our method over only $k$ shot trained model is in
brackets. The LSID model trained with full Nikon dataset ($k$=53) achieves
30.74dB PSNR and 0.803 SSIM and when trained with full Canon dataset ($k$=44)
attains 32.32dB PSNR and 0.899 SSIM (see Table 2).

Nikon as target | PSNR | SSIM
---|---|---
$k$ ($\rightarrow$) | 1 | 2 | 4 | 1 | 2 | 4
| LSID
---
(only $k$ target)
23.20 $\pm$ 3.06 | 27.27 $\pm$ 0.384 | 28.05 $\pm$ 1.53 | 0.679 $\pm$ 0.172 | 0.819 $\pm$ 0.031 | 0.864 $\pm$ 0.011
| Proposed
---
($k$ target + source)
|  25.27 $\pm$ 0.58
---
(+2.07)
|  28.06 $\pm$ 0.671
---
(+0.79)
|  30.30 $\pm$ 0.52
---
(+2.25)
|  0.860 $\pm$ 0.010
---
(+0.181)
|  0.909 $\pm$ 0.003
---
(+0.090)
|  0.913 $\pm$ 0.006
---
(+0.049)
Canon as target |  |  |  |  |  |
$k$ ($\rightarrow$) | 1 | 3 | 6 | 1 | 3 | 6
| LSID
---
(only $k$ target)
21.54 $\pm$ 2.89 | 26.9 $\pm$ 2.37 | 29.36 $\pm$ 0.763 | 0.588 $\pm$ 0.182 | 0.785 $\pm$ 0.005 | 0.829 $\pm$ 0.007
| Proposed
---
($k$ target + source)
|  24.29 $\pm$ 3.16
---
(+2.75)
|  28.78 $\pm$ 3.54
---
(+1.8)
|  33.22 $\pm$ 0.45
---
(+3.86)
|  0.623 $\pm$ 0.007
---
(+0.035)
|  0.841 $\pm$ 0.033
---
(+0.056)
|  0.896 $\pm$ 0.015
---
(+0.067)
|  |  |  |  |  |

For few-shot domain adaptation, we work with very few (< 10) target camera
images in every training experiment. We use the open-source Canon camera low-
light raw image dataset [Can()] and a new Nikon camera dataset that we have
compiled and make available with this work for our target camera training
pipeline. We do not investigate burst denoising or the ‘lucky imaging’
phenomenon. Hence, we only take the first short-exposure raw image for each
scene from the Sony dataset and use the 161 images for our source camera
training pipeline. Note that the Canon dataset has eight different ratios with
close ranges such that they can be put into three buckets of ratios: 50, 150,
and 300 (Table 2).

Nikon camera dataset: We have compiled a dataset of raw low-light images
captured with a Nikon D5600 camera to train the proposed few-shot domain
adaptation architecture. The Nikon dataset consists of short-exposure images
captured at $\nicefrac{{1}}{{3}}$ or $\nicefrac{{1}}{{10}}$ seconds and
corresponding ground-truth long-exposure images captured at 10 or 30 seconds
in the NEF format. For uniformity, there are two short-exposure images for
every long-exposure image such that the exposure ratio (ratio of exposure time
between the ground-truth long-exposure image and the input short-exposure
image) is 100 and 300, respectively. Similar to [Chen et al.(2018a)Chen, Chen,
Xu, and Koltun], we mount the camera on sturdy tripods and use appropriate
camera settings to capture the static scenes using a smartphone app. The
images captured include 129 short-exposure and 65 long-exposure ground-truth
images of indoor and outdoor low-light scenes (sub lux).

Training Setup: We train the source and target pipelines simultaneously in an
end-to-end manner. As discussed in section 3, we use the respective short-
exposure raw images as the input to each of the encoders. We first randomly
crop a $512\times 512$ image patch and augment it with random-flip and random-
rotate. We then subtract the black level and multiply the input raw image with
the exposure ratio. We use an initial learning rate of $10^{-4}$ up to 2000
epochs and then reduce it by a factor of 10 for every 1000 epochs thereafter.
We use the Adam [Kingma and Ba(2014)] optimizer for the 8-bit SSIM loss and
Cosine Similarity loss ($\mathcal{L}_{source}$) for the source pipeline, and
the $\mathcal{L}_{target}$ loss for the target pipeline.

We train the model for 4000 epochs (same as [Chen et al.(2018a)Chen, Chen, Xu,
and Koltun]) but observe the loss saturating at lower epochs prompting us to
employ early stopping. Since the large source domain has 161 images, every
epoch has 161 train steps. As we jointly train the source and target
pipelines, for every epoch, we use 161 randomly cropped source patches
obtained from the 161 source domain raw images and 161 randomly cropped target
patches from only $k$-images in the target domain. We find that training our
proposed method for up to 2500 epochs is sufficient to obtain the best results
and reproduce the results in this paper for few-shot domain adaptation.

The source SSIM loss is calculated in the 8-bit space after passing the output
from the shared $\mathbb{N}$ through the 16-to-8-bit converter. The cosine
similarity loss for the source domain and $L_{1}$ loss for the target domain
are computed in the 16-bit sRGB space. The exposure ratio is computed and
provided to the network. At inference time, we use the full-scale raw target
image as input to the target camera pipeline and obtain the enhanced target
sRGB image.

##  5 Results

Figure 5: Qualitative comparison of results for $k$=1,2 and 4 for Sony as
source and Nikon as target, and $k$=1,3 and 6 for Sony as source and Canon as
target for choosing the value of $k$. Table 4: Comparison with baselines
trained with Sony raw data as source and fine-tuned with (1/2/4-shot) Nikon
camera raw images and (1/3/6-shot) Canon camera raw images. We compare with
HDRCNN [Eilertsen et al.(2017)Eilertsen, Kronander, Denes, Mantiuk, and
Unger], Unprocess [Brooks et al.(2019)Brooks, Mildenhall, Xue, Chen, Sharlet,
and Barron] and LSID [Chen et al.(2018a)Chen, Chen, Xu, and Koltun].

| Method | Sony source w/ Nikon target
---|---
$k$ ($\rightarrow$) | 1 | 2 | 4
| PSNR | SSIM | PSNR | SSIM | PSNR | SSIM
HDRCNN | 11.47 | 0.648 | 12.36 | 0.578 | 12.87 | 0.627
Unprocess | 18.90 | 0.710 | 21.91 | 0.690 | 25.63 | 0.761
LSID | 22.38 | 0.746 | 25.75 | 0.874 | 27.93 | 0.899
Proposed | 25.27 | 0.860 | 28.06 | 0.909 | 30.30 | 0.913
| Sony source w/ Canon target
---
1 | 3 | 6
PSNR | SSIM | PSNR | SSIM | PSNR | SSIM
15.07 | 0.593 | 15.89 | 0.627 | 16.14 | 0.633
16.97 | 0.609 | 22.15 | 0.671 | 27.20 | 0.733
25.88 | 0.792 | 28.38 | 0.831 | 28.85 | 0.819
24.29 | 0.623 | 28.78 | 0.841 | 33.22 | 0.896

Table 5: Comparison with recent low-light image enhancement methods. Ours is
the first few-shot domain adaptation method, hence, we first train the
baselines on the Sony camera dataset and then fine-tune with (4-shot) Nikon
camera or (6-shot) Canon camera datasets.

Method | Sony Source | Sony w/ Nikon | Sony w/ Canon
---|---|---|---
| PSNR | SSIM | PSNR | SSIM | PSNR | SSIM
DeepUPE [Wang et al.(2019)Wang, Zhang, Fu, Shen, Zheng, and Jia] | 14.58 | 0.256 | 13.42 | 0.266 | 13.81 | 0.285
MIRNet [Zamir et al.(2020)Zamir, Arora, Khan, Hayat, Khan, Yang, and Shao] | 15.24 | 0.414 | 14.18 | 0.458 | 13.24 | 0.397
KnD [Zhang et al.(2019)Zhang, Zhang, and Guo] | 17.15 | 0.313 | 15.04 | 0.226 | 17.24 | 0.432
HDRCNN [Eilertsen et al.(2017)Eilertsen, Kronander, Denes, Mantiuk, and Unger] | 17.39 | 0.491 | 12.87 | 0.627 | 16.14 | 0.633
KnD++ [Zhang et al.(2021)Zhang, Guo, Ma, Liu, and Zhang] | 23.03 | 0.579 | 19.38 | 0.471 | 21.25 | 0.434
Unprocess [Brooks et al.(2019)Brooks, Mildenhall, Xue, Chen, Sharlet, and Barron] | 27.83 | 0.700 | 25.63 | 0.761 | 27.20 | 0.733
LSID [Chen et al.(2018a)Chen, Chen, Xu, and Koltun] | 28.50 | 0.774 | 27.93 | 0.899 | 29.36 | 0.829
Proposed | - | - | 30.30 | 0.913 | 33.22 | 0.896

Table 2 lists the total number of labeled raw image pairs in the train set to
be 161 images for the Sony dataset [Chen et al.(2018a)Chen, Chen, Xu, and
Koltun], 53 images for our Nikon dataset, and 44 images for the Canon dataset
[Can()]. We train our model for three different numbers of labeled target
data: For Nikon target, we use $k$=1,2, and 4. For Canon target, we use
$k$=1,3, and 6. We choose the $k$ based on the different exposure ratios
available for the dataset. As reported in Table 2, the Nikon camera dataset
has two exposure ratios; hence we use two images per ratio leading to a total
of four images. Similarly, the Canon camera dataset has three ratios; hence we
use two images per ratio leading to a total of six images. We observe that
using two images per exposure ratio in the target domain is sufficient to
outperform all baselines (Refer Table 3). A qualitative guideline for choosing
the value of $k$ is in Fig.(5). For each $k$, we run three separate
experiments, each with a different set of $k$ labeled target images. We report
the average and 95% variance margin computed across three different sets for
the Nikon dataset and for the Canon dataset (Table 3). We compare with an LSID
model trained with only $k$-target data and an LSID model trained with the
full target camera data.

Quantitative Evaluation: For Nikon dataset as target, our 4-shot approach
achieves 30.30dB PSNR, which is on par with the full target dataset ($k$=53)
trained LSID model. Our method outperforms only $k$-shot trained model by
2.25dB PSNR (Table 3). Similarly, for Canon dataset as target, our 6-shot
approach outperforms the full dataset ($k$=44) trained LSID model by 0.9dB
PSNR, and $k$-shot trained LSID model by 3.86dB PSNR (Table 3). Since ours is
the first few-shot domain adaptation method, we quantitatively compare our
method with baselines by training them on the full Sony source dataset and
then fine-tuning them in a few-shot manner on the Nikon or Canon target
datasets (Table 4). We also compare with recent low-light enhancement methods
in Table 5 and show that our method outperforms all $k$-shot fine-tuned
baselines in PSNR and SSIM for the Nikon and Canon target datasets. We show
quantitative results for our method trained with Sony as source and four
OnePlus camera images or four Google Pixel camera images as target in Table 6.

As discussed in section 1, capturing a low-light raw image dataset is
difficult, and different cameras have different color-space and noise
distributions, hence there is a need for a domain adaptation method that can
tranfer the task from source to a target domain in a few-shot setting. Despite
the high complexity of the task, our method outperforms all baselines with a
lightweight model because the abundant source data helps to learn the low-
light enhancement task in the source+$k$-shot setting successfully as compared
to using only target domain data. While the $k$-shot samples help to predict
the output in the target domain, the task is transferred successfully from the
large source domain to the target camera domain.

Qualitative Evaluation: In Fig. 4, we qualitatively compare the results from
our method and baselines when trained with Sony as source and Nikon (top row)
or Canon (bottom row) as target. As highlighted by the zoomed-in regions and
red arrows, the baseline results have several artifacts in terms of noise and
color. Although the LSID model trained with full target data performs better
than the $k$-shot model (for Nikon or Canon as target), it is still sub-par
compared to our method’s results. We also show qualitative results for our
method trained with Sony as source and four OnePlus camera images (Fig. 6(a))
or four Google Pixel camera images (Fig. 6(b)) as target to demonstrate the
effectiveness of our method on low-cost smartphone camera data, which
typically have higher noise severity in low-light.

|
---|---
(a) | (b)
Figure 6: Results for Sony as source with (a) OnePlus 5 as target (b) Google
Pixel as target. Table 6: Quantitative evaluation of Sony as source with
OnePlus or Pixel as target. The LSID model trained with full OnePlus dataset
($k$=12) achieves 28.88dB PSNR and 0.708 SSIM and when trained with full Pixel
dataset ($k$=10) attains 27.95dB PSNR and 0.759 SSIM. (Note: test set for
OnePlus=38 images and Pixel=18 images).

| Method | Sony source w/ OnePlus target
---|---
$k$ ($\rightarrow$) | 1 | 2 | 4
| PSNR | SSIM | PSNR | SSIM | PSNR | SSIM
LSID | 22.54 | 0.617 | 25.87 | 0.651 | 26.69 | 0.670
Proposed | 25.10 | 0.633 | 26.47 | 0.650 | 27.77 | 0.712
| Sony source w/ Pixel target
---
1 | 2 | 4
PSNR | SSIM | PSNR | SSIM | PSNR | SSIM
13.86 | 0.296 | 21.91 | 0.695 | 27.62 | 0.760
18.60 | 0.635 | 24.98 | 0.753 | 29.60 | 0.782

Table 7: Ablation study for training with Sony source and 4-shot Nikon as
target dataset. The table reports mean PSNR/SSIM over three different runs.
See section 7 for details.

No. | Details | PSNR | SSIM
---|---|---|---
1 | Separate encoder and decoder | 28.62 | 0.867
2 | Combined encoder | 29.20 | 0.890
3 | Proposed w/ Source $\ell_{1}$ loss | 27.14 | 0.807
4 | Proposed w/o Source SSIM loss | 29.38 | 0.902
5 | Proposed | 30.30 | 0.913

Ablation Study: We discuss relevant ablations for our proposed method trained
with Sony as source and 4-shot Nikon as target (Refer Table 7). The details
are as follows:

  * •

Separate Encoder and Decoder: Transfer learning methods performed sub-
optimally primarily due to the color bias. Hence, we trained separate
encoders, a shared U-net, and separate camera-specific decoders to learn
different camera color spaces individually. This approach obtained a PSNR of
28.62dB and SSIM of 0.867, and the results had visible color gaps between the
model’s output and ground truth.

  * •

Combined Encoders: To verify camera-specific denoising by the encoders, we
used a shared encoder followed by a shared $\mathbb{N}$ network. But, we
noticed the colors of the target domain’s output to be dominated by the large
source domain data. We obtain a PSNR of 29.20dB and SSIM of 0.890, which is
better than fine-tuning LSID, but the colors of the target domain’s output
validation images were dominated by the source training images.

  * •

Proposed w/ Source $\mathcal{\ell}_{1}$: Training with a strong supervision
loss for the source domain strongly influenced the colors of the target
domain’s output images.

  * •

Proposed w/o $\mathcal{L}_{SSIM}$: From previous ablations, we observe the
source domain influencing the target output image color. Hence, we separate
only the encoder while merging the U-net and the decoder to get
($\mathbb{N}$). We experimented with several loss functions, including
combinations of $\ell_{2}$ loss, grayscale SSIM, gradient loss, $\ell_{1}$
loss, and cosine similarity loss. We found that using cosine similarity loss
and SSIM loss for the source domain, and $\ell_{1}$ loss [Zhao et
al.(2017)Zhao, Gallo, Frosio, and Kautz] for the target led to better
preservation of color and structure information.

  * •

The $\mathcal{D}$ network for SSIM loss: As discussed in section 3, type-
casting the source output from 16-bit to 8-bit space will still possess domain
specific details. The SSIM loss is used to compare the brightness and
structural details but not the color quality (cosine similarity is for color).
Thus, following the camera ISP processing steps, we train a 16-to-8-bit
conversion U-net model ($\mathcal{D}$ in Fig. 2(b)) to convert the 16-bit data
to 8-bit data. The $\mathcal{D}$ network is trained to perform the following
non-linear operations: White balancing, Gamma correction, Quantization and
JPEG compression. From experiments, we observe that a U-net is necessary to
learn all the above mentioned non-linear operations.

##  6 Conclusion

In this work, we propose a novel domain adaptation method for low-light raw
image enhancement using only a few labeled samples from the target domain and
many source domain samples. We first extract camera-specific information
through separate encoders, and then use a shared enhancement network
($\mathbb{N}$) to extract domain invariant features and transfer the task
successfully from the source domain to the target domain. We propose to
compute post-processed image loss for the source domain to have less influence
on predictions in the target domain. We also present a new labeled raw image
dataset captured with a Nikon camera. Our results show that using only a few
labeled samples from the target domain is sufficient to obtain similar or
better results than training with large target domain data. We hope that our
method and dataset inspire new investigations along these research directions.

Acknowledgment: This work was supported by a project grant from MeitY
(No.4(16)/2019-ITEA), Govt. of India.

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