Enhancement of a CNN-Based Denoiser Based on Spatial and Spectral Analysis

• URL: http://arxiv.org/abs/2006.15517v1
• Date: Sun, 28 Jun 2020 05:25:50 GMT
• Title: Enhancement of a CNN-Based Denoiser Based on Spatial and Spectral Analysis
• Authors: Rui Zhao, Kin-Man Lam, Daniel P.K. Lun
• Abstract summary: We propose a discrete wavelet denoising CNN (WDnCNN) which restores images corrupted by various noise with a single model. To address this issue, we present a band normalization module (BNM) to normalize the coefficients from different parts of the frequency spectrum. We evaluate the proposed WDnCNN, and compare it with other state-of-the-art denoisers.
• Score: 23.11994688706024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional neural network (CNN)-based image denoising methods have been widely studied recently, because of their high-speed processing capability and good visual quality. However, most of the existing CNN-based denoisers learn the image prior from the spatial domain, and suffer from the problem of spatially variant noise, which limits their performance in real-world image denoising tasks. In this paper, we propose a discrete wavelet denoising CNN (WDnCNN), which restores images corrupted by various noise with a single model. Since most of the content or energy of natural images resides in the low-frequency spectrum, their transformed coefficients in the frequency domain are highly imbalanced. To address this issue, we present a band normalization module (BNM) to normalize the coefficients from different parts of the frequency spectrum. Moreover, we employ a band discriminative training (BDT) criterion to enhance the model regression. We evaluate the proposed WDnCNN, and compare it with other state-of-the-art denoisers. Experimental results show that WDnCNN achieves promising performance in both synthetic and real noise reduction, making it a potential solution to many practical image denoising applications.

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