  Image denoising is a classical problem in low level computer vision.
Model-based optimization methods and deep learning approaches have been the two
main strategies for solving the problem. Model-based optimization methods are
flexible for handling different inverse problems but are usually
time-consuming. In contrast, deep learning methods have fast testing speed but
the performance of these CNNs is still inferior. To address this issue, here we
propose a novel deep residual learning model that combines the dilated residual
convolution and multi-scale convolution groups. Due to the complex patterns and
structures of inside an image, the multiscale convolution group is utilized to
learn those patterns and enlarge the receptive field. Specifically, the
residual connection and batch normalization are utilized to speed up the
training process and maintain the denoising performance. In order to decrease
the gridding artifacts, we integrate the hybrid dilated convolution design into
our model. To this end, this paper aims to train a lightweight and effective
denoiser based on multiscale convolution group. Experimental results have
demonstrated that the enhanced denoiser can not only achieve promising
denoising results, but also become a strong competitor in practical
application.
