Modified convolutional neural network with pseudo-CNN for removing nonlinear noise in digital images

Abstract

Recently, deep learning techniques are widely used in various computer vision applications such as pattern recognition, data classification, object detection, image enhancement, etc. Deep learning frameworks outperform classical algorithms due to their flexibility and interoperability. The main aim of this work is to remove Gaussian-Impulse noise with blind (unknown) noise densities in digital images. The proposed work has two phases; in phase 1, the noisy image is preprocessed with a novel Pseudo–Convolutional Neural Network (P-CNN) having no tunable parameters. The Pseudo-CNN is a customized three-layered network inspired by conventional convolutional neural networks. The first-level feature detector has k number of (2d+1)×(2d+1) Weight Initialized Adaptive Window (WIAW) filters. The weights or coefficients of WIAW filters are initialized using the noise probabilistic distribution. The loss function is not estimated in P-CNN because there is only one forward pass and no backpropagation. The PCNN has excellent Impulse noise rejection capability. In phase 2, a modified Convolutional Neural Network (CNN) is applied to the preprocessed image to obtain a latent clean image. To improve the network performance, we have utilized residual learning and batch normalization. The high degree of localized pixel correlation established by P-CNN helps the modified CNN to learn compact representations and salient features of the input image. The proposed method not only denoises images corrupted with blind noise levels of Gaussian-Impulse noise but is also effective for removing Impulse noise in digital images. Our experimental results imply that the proposed method gives good qualitative and quantitative results compared to various state-of-the-art techniques. Convolutional neural networks are pertinent for parallel computation using powerful GPUs, which help to improve the denoising performance.