A front-loaded transposed convolution plus recurrent residual blocks and three-level fusion yields modest PSNR gains (up to 0.24 dB) over older SR networks at x4 and x8, though state-of-the-art comparisons are incomplete.
Image super-resolution using deep convolutional networks,
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DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution with Large Factors
A front-loaded transposed convolution plus recurrent residual blocks and three-level fusion yields modest PSNR gains (up to 0.24 dB) over older SR networks at x4 and x8, though state-of-the-art comparisons are incomplete.