  One popular strategy for image denoising is to design a generalized
regularization term that is capable of exploring the implicit prior underlying
data observation. Convolutional neural networks (CNN) have shown the powerful
capability to learn image prior information through a stack of layers defined
by a combination of kernels (filters) on the input. However, existing CNN-based
methods mainly focus on synthetic gray-scale images. These methods still
exhibit low performance when tackling multi-channel color image denoising. In
this paper, we optimize CNN regularization capability by developing a kernel
regulation module. In particular, we propose a kernel regulation network-block,
referred to as KR-block, by integrating the merits of both large and small
kernels, that can effectively estimate features in solving image denoising. We
build a deep CNN-based denoiser, referred to as KRNET, via concatenating
multiple KR-blocks. We evaluate KRNET on additive white Gaussian noise (AWGN),
multi-channel (MC) noise, and realistic noise, where KRNET obtains significant
performance gains over state-of-the-art methods across a wide spectrum of noise
levels.
