Abstract
Recent works tend to design effective but deep and complex denoising networks, which usually ignored the industrial requirement of efficiency. In this paper, an effective and efficient self-refinement and reconstruction network (SRRNet) is proposed for image denoising. It is based on the encoder-decoder architecture and three improvements are introduced to solve the problem. Specifically, four novel residual connections of different types are proposed as building blocks to maintain original contextual details. A high-resolution reconstruction module is introduced to connect cross-level encoders and corresponding decoders, so as to boost information flow and result in realistic clear image. And multi-scale dual attention is used for suppressing noise and enhancing beneficial dependency. SRRNet achieves PSNR of 39.83 dB and 39.96 dB on SIDD and DND respectively. Compared with other works, the accuracy is higher and the complexity is lower. Extensive experiments in real-world image denoising and Gaussian noise removal prove that SRRNet balances performance and temporal cost better.
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