This paper presents a novel Res2Net multi-scale image super-resolution (SR) network in the curvelet transform domain based on deep learning, namely curvelet-Res2Net. Firstly, we integrate Res2Net block to adaptively increase the range of receptive fields for each network layer and exploit its potential multi-scale ability at a granular level from images. And then, a sequence of cascaded Res2Net blocks is formed into curvelet-Res2Net. In addition, the image SR problem is formulated as curvelet coefficient prediction to preserve richer structure details to overcome the drawbacks of oversmoothed outputs and poor texture details of most previous high resolution (HR) image predication methods in the spatial domain. Three standard image datasets are used qualitatively and quantitatively verify the performance of our method, and the results show its capability of achieving higher Peak Signal-to-Noise Ratios (PSNRs) and Structural SIMilarity (SSIM) while preserving much more useful information, comparing with other methods.
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