Image Super Resolution Based on Fusing Multiple Convolution Neural Networks

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Fast, Accurate, and, Lightweight Super-Resolution with Cascading Residual Network (2018, methodology): “Because of the richness of this dataset, recent SR models [4, 8, 26, 31] use DIV2K as well.”
Deep Learning for Image Super-Resolution: A Survey (2019, methodology): “[77] feature similarity MS-SSIM [74] multi-scale SSIM IFC [75] information ﬁdelity criterion PSNR peak signal-to-noise ratio IQA image quality assessment SSIM [62] structural similarity MOS mean opinion score VIF [76] visual information ﬁdelity MSSIM [62] mean SSIM BN [140] batch normalization GAN [”
Densely Residual Laplacian Super-Resolution (2019, methodology): “Ren et al. [25] employ SRCNN [28] at various branches with a different number of layers to learn features uniquely and lastly combine them using a sum-pooling layer.”
A Deep Journey into Super-resolution (2019, background): “ardbackward steps during training. Multi-branch designs are becoming common in several other computer vision tasks as well. We explain multi-branch networks in the section below. 3.6.1 CNF Ren et al. [64] proposed fusing multiple convolutional neural networks for image super-resolution. The authors ”