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Removal of impulse noise in color images based on convolutional neural network

2019; Elsevier BV; Volume: 82; Linguagem: Inglês

10.1016/j.asoc.2019.105558

1872-9681

Wenhua Zhang, Lianghai Jin, Enmin Song, Xiangyang Xu,

Advanced Image Processing Techniques

A new denoising framework based on deep convolutional neural network for suppressing impulse noise in color images is proposed in this paper. The proposed framework consists of two modules: noise detection and image reconstruction, both of which are implemented by a deep convolutional neural network. First, a noise classifier network is trained to detect random-valued impulse noise in a color image, which not only can detect the noisy color vector pixels but also can further identify the corrupted channels of each noisy color pixel. Then, a sparse clean color image is computed by replacing the values of noisy channels with 0 and keeping other noise-free channels unchanged. Finally, the sparse clean color image is fed to another denoiser network to reconstruct the denoised image. Experimental results show that the proposed denoiser outperforms other state-of-the-art methods clearly in both performance measure and visual evaluation.