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

TL;DR

This paper introduces WDnCNN, a wavelet-based CNN denoiser that normalizes frequency bands and uses discriminative training to effectively reduce synthetic and real noise in images.

Contribution

The paper proposes a novel wavelet-based CNN denoiser with band normalization and discriminative training, improving performance on real-world noise reduction tasks.

Findings

Achieves superior denoising performance on synthetic and real noise datasets.

Effectively handles spatially variant noise with a single model.

Outperforms existing state-of-the-art denoisers in experiments.

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.