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We propose a real-time and lightweight single-image super-resolution (SR)network named Bicubic++. Despite using spatial dimensions of the input imageacross the whole network, Bicubic++ first learns quick reversible downgradedand lower resolution features of the image in order to decrease the number ofcomputations. We also construct a training pipeline, where we apply anend-to-end global structured pruning of convolutional layers without usingmetrics like magnitude and gradient norms, and focus on optimizing the prunednetwork's PSNR on the validation set. Furthermore, we have experimentally shownthat the bias terms take considerable amount of the runtime while increasingPSNR marginally, hence we have also applied bias removal to the convolutionallayers. Our method adds ~1dB on Bicubic upscaling PSNR for all tested SRdatasets and runs with ~1.17ms on RTX3090 and ~2.9ms on RTX3070, for 720pinputs and 4K outputs, both in FP16 precision. Bicubic++ won NTIRE 2023 RTSRTrack 2 x3 SR competition and is the fastest among all competitive methods.Being almost as fast as the standard Bicubic upsampling method, we believe thatBicubic++ can set a new industry standard.

Bicubic++: Slim, Slimmer, Slimmest -- Designing an Industry-Grade Super-Resolution Network