Open Access

ARTICLE

Image Denoising with GAN Based Model

Peizhu Gong, Jin Liu^*, Shiqi Lv

College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China

* Corresponding Author: Jin Liu. Email:

Journal of Information Hiding and Privacy Protection 2020, 2(4), 155-163. https://doi.org/10.32604/jihpp.2020.010453

Received 05 August 2020; Accepted 12 September 2020; Issue published 07 January 2021

Abstract

Image denoising is often used as a preprocessing step in computer vision tasks, which can help improve the accuracy of image processing models. Due to the imperfection of imaging systems, transmission media and recording equipment, digital images are often contaminated with various noises during their formation, which troubles the visual effects and even hinders people’s normal recognition. The pollution of noise directly affects the processing of image edge detection, feature extraction, pattern recognition, etc., making it difficult for people to break through the bottleneck by modifying the model. Many traditional filtering methods have shown poor performance since they do not have optimal expression and adaptation for specific images. Meanwhile, deep learning technology opens up new possibilities for image denoising. In this paper, we propose a novel neural network which is based on generative adversarial networks for image denoising. Inspired by U-net, our method employs a novel symmetrical encoder-decoder based generator network. The encoder adopts convolutional neural networks to extract features, while the decoder outputs the noise in the images by deconvolutional neural networks. Specially, shortcuts are added between designated layers, which can preserve image texture details and prevent gradient explosions. Besides, in order to improve the training stability of the model, we add Wasserstein distance in loss function as an optimization. We use the peak signal-to-noise ratio (PSNR) to evaluate our model and we can prove the effectiveness of it with experimental results. When compared to the state-of-the-art approaches, our method presents competitive performance.

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Keywords

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