Activating Wider Areas in Image Super-Resolution

arXiv:2403.08330v1 Announce Type: new
Abstract: The prevalence of convolution neural networks (CNNs) and vision transformers (ViTs) has markedly revolutionized the area of single-image super-resolution (SISR). To further boost the SR performances, several techniques, such as residual learning and attention mechanism, are introduced, which can be largely attributed to a wider range of activated area, that is, the input pixels that strongly influence the SR results. However, the possibility of further improving SR performance through another versatile vision backbone remains an …

abstract arxiv attention boost cnns convolution cs.cv image networks neural networks performances residual transformers type vision vision transformers