Image denoising based on learning sparse coding network

Ruihong Cheng; Huajun Wang

doi:10.1117/12.2600357

1 June 2021 Image denoising based on learning sparse coding network

Ruihong Cheng, Huajun Wang

Proceedings Volume 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021); 1184809 (2021) https://doi.org/10.1117/12.2600357
Event: International Conference on Signal Image Processing and Communication (ICSIPC 2021), 2021, Chengdu, China

Abstract

This work presents a novel image denoising architecture based on learning sparse coding network. Our network is inspired by learning iterate soft thresholds algorithm (LISTA) and sparse coding network (SCN). By doing this, the training parameters are reduced effectively, and training process is speeded up. We attempt to use this architecture to get a clear image from the noisy overlapping image patches. Finally, we used adaptive weights to fuse the image patches as whole image. Compared with existing denoising methods, the proposed method achieves state-of-the-art performance in image denoising

Citation Download Citation

Ruihong Cheng and Huajun Wang "Image denoising based on learning sparse coding network", Proc. SPIE 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021), 1184809 (1 June 2021); https://doi.org/10.1117/12.2600357

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