MLFAN: Multilevel Feature Attention Network With Texture Prior for Image Denoising

Machine learning techniques, especially deep learning, have made great achievements in computer vision including image denoising recently. However, in most convolutional neural network (CNN) based methods presented for image denoising, convolutional kernels are considered for only one scale and more scales are neglected mostly. Studies on multilevel feature extraction treat these features as if they have the same importance and do not use a mechanism such as feature attention for their weighting. Also, for effective noise removal, edge information is used as prior knowledge, but texture information is generally disregarded. This study has focused on these shortcomings and introduced a new attention-based CNN for image denoising. The main contributions of this study are as follows: First, we propose a CNN-based network to extract Local Binary Pattern (LBP) from the noisy image for texture information. So, we use texture information as prior knowledge for the preservation of details in the evolved image during the denoising process. Besides we propose a new multilevel feature extraction block to get different level features. After extracting multilevel features using feature attention, we weight these different levels of features. In addition to this, we introduce a multilevel feature attention network (MLFAN) for noise removal by combining them. The comprehensive experimental results show that our MLFAN noise reduction network can effectively remove Gaussian noise from images and compared with some state-of-the-art denoising methods, it outperforms in terms of both quantitative and qualitative evaluations. For Set12 grey image set, and McMaster color image set, MLFAN gives PSNR = {33.08, 30.75, 27.56}, SSIM = {0.9087, 0.8702, 0.7939} and PSNR = {35.08, 32.68, 29.47}, SSIM = {0.9288, 0.8956, 0.8263} respectively for noise level s = {15, 25, 50}.

Automated summary

In this study, an attention-based CNN is introduced for image denoising. The network extracts texture information for detail preservation and utilizes multilevel feature extraction and weighting for noise removal. Experimental results demonstrate that the proposed method outperforms state-of-the-art denoising methods in terms of both quantitative and qualitative evaluations.