RRGs, each of which further contains $2$ MRBs. The MRB consists of $3$ parallel streams with channel dimensions of $64, 128, 256$ at resolutions $1, \frac{1}{2}, \frac{1}{4}$, respectively. Each stream has $2$ DAUs. The models are trained with the Adam optimizer ($\beta_1 = 0.9$, and $\beta_2=0.999$) for $7\times10^5$ iterations. The initial learning rate is set to $2\times10^{-4}$. We employ the cosine annealing strategy [@loshchilov2016sgdr] to steadily decrease the learning rate from initial value to $10^{-6}$ during training. We extract patches of size $128\times128$ from training images. The batch size is set to $16$ and, for data augmentation, we perform horizontal and vertical flips.

Image Denoising
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In this section, we demonstrate the effectiveness of the proposed MIRNet for image denoising. We train our network only on the training set of the SIDD [@sidd] and directly evaluate it on the test images of both SIDD and DND [@dnd] datasets. Quantitative comparisons in terms of PSNR and SSIM metrics are summarized in Table \[table:sidd\] and Table \[table:dnd\] for SIDD and DND, respectively. Both tables show that our MIRNet performs favourably against the data-driven, as well as conventional, denoising algorithms. Specifically, when compared to the recent best method VDN [@VDN], our algorithm demonstrates 