SIDD [@sidd]. Our method effectively removes real noise from challenging images, while better recovering structural content and fine texture.[]{data-label="fig:sidd example"}](Images/Denoising/SIDD/RGB1/SSID_3828_Ours_MSRNet "fig:"){width=".16\textwidth"}   ![Denoising examples from SIDD [@sidd]. Our method effectively removes real noise from challenging images, while better recovering structural content and fine texture.[]{data-label="fig:sidd example"}](Images/Denoising/SIDD/RGB1/SSID_3828_gt "fig:"){width=".16\textwidth"}  
                                                                                                                                        18.16 dB                                                                                                                                                                                                                                                                             20.36 dB                                                                                                                                                                                                                                                                              29.83 dB                                                                                                                                                                                                                                                                             30.31 dB                                                                                                                                                                                                                                                                              **31.36 dB**                                                                                                                                                                                                                                                                                                                                                                                                                            
                                                                                                                                          Noisy                                                                                                                                                                                                                                                                          CBDNet [@CBDNet]                                                                                                                                                                                                                                                                      RIDNet [@RIDNet]                                                                                                                                                                                                                                                                        VDN [@VDN]                                                                                                                                                                                                                                                                             MIRNet (Ours)                                                                                                                                                                                                                                                                            Reference                                                                                                                                      
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**Generalization capability.** The DND and SIDD datasets are acquired with different sets of cameras having different noise characteristics. Since the DND benchmark does not provide training data, setting a new state-of-the-art on DND with our SIDD trained network indicates the good generalization capability of our approach.

Super-Resolution (SR)
---------------------

We compare our MIRNet against the state-of-the-art SR algorithms (VDSR [@VDSR], SRResNet [@SRResNet], RCAN [@RCAN], LP-KPN [@RealSR]) on the testing images of the RealSR [@RealSR] for upscaling factors of $\times2$, $\times3$ and $\times4$. Note that all the benchmarked algorithms are trained on the RealSR [@RealSR] dataset for fair comparison. In the experiments, we also include bicubic interpolation [@keys1981cubic], which is the most commonly used method for generating super-resolved images. Here, we compute the PSNR and SSIM scores using the 