Deep Shrinkage Convolutional Neural Network for Adaptive Noise Reduction
Kenzo Isogawa, Takashi Ida, Taichiro Shiodera, Tomoyuki Takeguchi · IEEE Signal Processing Letters · 2017
The noise level of an image depends on settings of an imaging device. The settings can be used to select appropriate parameters for denoising methods. But denoising methods based on deep convolutional neural networks (deep-CNN) do not have such adjustable parameters. Therefore, a deep-CNN whose training data contain limited levels of noise does not effectively restore images whose noise level is different from the training data. If the range of noise levels of training data is extended to solve the problem, the maximum performance of a produced deep-CNN is limited. To solve the tradeoff, we propose a deep-CNN that is adjustable to the noise level of the input image immediately. We use soft shrinkage for activation functions of our deep-CNN. The soft shrinkage has thresholds proportional to the noise level given by the user. We also propose an optimization method for proportionality coefficients for the thresholds of soft shrinkage. Our method optimizes the coefficients for various noise levels simultaneously. In our experiment using a test set whose noise level is from 5 to 50, the proposed method showed higher PSNR than that in the case of the conventional method using only one deep-CNN, and PSNR comparable to that in the case of the conventional method using multiple noise-level-specific CNNs.