| 要約: | Deep learning-based networks have achieved great success in the field of image super-resolution. However, many networks do not fully combine high-level and low-level information, and fuse local and global information. A multiscale recursive feedback network (MSRFN) for image super-resolution is proposed. First, multiscale convolution is integrated into the feedback network to propose multiscale projection units that adaptively capture image features of different scales by driving a multipath information flow. Next, recursive learning is applied to multiscale projection groups composed of up- and down-multiscale projection units to construct a feedback module that exploits high-level information to correct the low-level representation and refines the features in the early layers. Then, global residual learning and local residual feedback were combined to provide more contextual information for the final reconstruction. Experimental results demonstrate that MSRFN can predict more high-frequency details and alleviate the ringing effect and checkerboard artifacts inherently in CNN-based models. Even when the training datasets are relatively small, MSRFN is still superior to most state-of-the-art methods, especially for large scaling factors (<inline-formula> <tex-math notation="LaTeX">$\times 8$ </tex-math></inline-formula>).
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