Curvelet-based image super-resolution via Res2Net multi-scale network

Wei Wei; Junchang Xin

doi:10.1117/12.2599675

30 June 2021 Curvelet-based image super-resolution via Res2Net multi-scale network

Wei Wei, Junchang Xin

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 118781C (2021) https://doi.org/10.1117/12.2599675
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

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.

Citation Download Citation

Wei Wei and Junchang Xin "Curvelet-based image super-resolution via Res2Net multi-scale network", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 118781C (30 June 2021); https://doi.org/10.1117/12.2599675

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