论文标题
基于空间和光谱分析的基于CNN的DeNoiser的增强
Enhancement of a CNN-Based Denoiser Based on Spatial and Spectral Analysis
论文作者
论文摘要
由于其高速处理能力和良好的视觉质量，最近对基于卷积的神经网络（CNN）基于图像denoising方法进行了广泛研究。但是，大多数现有的基于CNN的Denoisers从空间域中学习了先前的图像，并遭受了空间变化噪声的问题，这限制了其在现实世界图像denoing任务中的性能。在本文中，我们提出了一个离散的小波denoising CNN（WDNCNN），该小波恢复了用单个模型恢复各种噪声的图像。由于自然图像的大多数内容或能量都存在于低频光谱中，因此它们在频域中转换的系数高度不平衡。为了解决此问题，我们提出一个频带归一化模块（BNM），以使来自频谱不同部分的系数正常化。此外，我们采用频段判别训练（BDT）标准来增强模型回归。我们评估了提出的WDNCNN，并将其与其他最先进的Denoisers进行了比较。实验结果表明，WDNCNN在综合和实际降低降噪方面都可以实现有希望的性能，从而使其成为许多实用图像denosing应用的潜在解决方案。
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.
