adjusting the focal-length of the cameras. RealSR have both indoor and outdoor images taken with two cameras. The number of training image pairs for scale factors $\times2$, $\times3$ and $\times4$ are $183$, $234$ and $178$, respectively. For each scale factor, $30$ test images are also provided in RealSR.

**Image enhancement.** **(1) LoL [@wei2018deep]** is created for low-light image enhancement problem. It provides 485 images for training and 15 for testing. Each image pair in LoL consists of a low-light input image and its corresponding well-exposed reference image. **(2) MIT-Adobe FiveK [@mit_fivek]** contains $5000$ images of various indoor and outdoor scenes captured with several DSLR cameras in different lighting conditions. The tonal attributes of all images are manually adjusted by five different trained photographers (labelled as experts A to E). Same as in [@hu2018exposure; @park2018distort; @wang2019underexposed], we also consider the enhanced images of expert C as the ground-truth. Moreover, the first 4500 images are used for training and the last 500 for testing.

Implementation Details
----------------------

The proposed architecture is end-to-end trainable and requires no pre-training of sub-modules. We train three different networks for three different restoration tasks. The training parameters, common to all experiments, are the following. We use 3 