can be seen that LP-KPN [@RealSR] has a tendency to over-enhance the contrast (cols. 1, 3, 4) and in turn causes loss of details near dark and high-light areas. In contrast, the proposed MIRNet successfully reconstructs structural patterns and edges (col. 2) and produces images that are natural (cols. 1, 4) and have better color reproduction (col. 5).

**Cross-camera generalization.** The RealSR [@RealSR] dataset consists of images taken with Canon and Nikon cameras at three scaling factors. To test the cross-camera generalizability of our method, we train the network on the training images of one camera and directly evaluate it on the test set of the other camera. Table \[table:realSR generalization\] demonstrates the generalization of competing methods for four possible cases: (a) training and testing on Canon, (b) training on Canon, testing on Nikon, (c) training and testing on Nikon, and (d) training on Nikon, testing on Canon. It can be seen that, for all scales, LP-KPN [@RealSR] and RCAN [@RCAN] shows comparable performance. In contrast, our MIRNet exhibits more promising generalization.

Image Enhancement
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In this section, we demonstrate the effectiveness of our algorithm by evaluating it for the image enhancement task. We report PSNR/SSIM values of our method 