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MISNet: Multi-Resolution Level Feature Interpolating Ultralight-Weight Residual Image Super Resolution Network

The design of ultralight-weight super-resolution convolutional neural networks capable of providing images with high visual quality is crucial in many real-world applications with limited power and storage capacity, such as mobile devices and portable cameras. In this paper, a new ultralight-weight super-resolution network, based on the idea of using multiresolution level feature interpolation in a residual framework, is developed. In the proposed network, the multiple resolution level interpolated features generated are fused and the resulting feature maps are added to the residual features obtained from a shallow convolutional neural network. The proposed network is applied to various benchmark datasets and is shown to outperform the state-of-the-art ultralight-weight image super-resolution networks existing in the literature.