qualitative and quantitative assessment of the results produced by our MIRNet and compare it with the previous best methods. Next, we describe the datasets, and then provide the implementation details. Finally, we report results for **(a)** image denoising, **(b)** super-resolution and **(c)** image enhancement on five real image datasets. The source code and trained models will be released publicly[^1].

Real Image Datasets
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**Image denoising.** **(1) DND [@dnd]** consists of $50$ images captured with four consumer cameras. Since the images are of very high-resolution, the dataset providers extract $20$ crops of size $512\times512$ from each image, yielding $1000$ patches in total. All these patches are used for testing (as DND does not contain training or validation sets). The ground-truth noise-free images are not released publicly, therefore the image quality scores in terms of PSNR and SSIM can only be obtained through an online server [@dndwebsite]. **(2) SIDD [@sidd]** is particularly collected with smartphone cameras. Due to the small sensor and high-resolution, the noise levels in smartphone images are much higher than those of DSLRs. SIDD contains $320$ image pairs for training and $1280$ for validation. **Super-resolution.** **(1) RealSR [@RealSR]** contains real-world LR-HR image pairs of the same scene captured by 