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Research on Image Denoising and Super-Resolution Reconstruction Technology of Multiscale-Fusion Images by Size Li; Pengjiang Qian; Xin Zhang; Aiguo Chen is a Computer Science article available to read on EtoBox.

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Image denoising and image super-resolution reconstruction are two important techniques for image processing. Deep learning is used to solve the problem of image denoising and super-resolution reconstruction in recent years, and it usually has better results than traditional methods. However, image denoising and super-resolution reconstruction are studied separately by state-of-the-art work. To optimally improve the image resolution, it is necessary to investigate how to integrate these two techniques. In this paper, based on Generative Adversarial Network (GAN), we propose a novel image denoising and super-resolution reconstruction method, i.e., multiscale-fusion GAN (MFGAN), to restore the images interfered by noises. Our contributions reflect in the following three aspects: (1) the combination of image denoising and image super-resolution reconstruction simplifies the process of upsampling and downsampling images during the model learning, avoiding repeated input and output images operations, and improves the efficiency of image processing. (2) Motivated by the Inception structure and introducing a multiscale-fusion strategy, our method is capable of using the multiple convolutio

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Author

Size Li; Pengjiang Qian; Xin Zhang; Aiguo Chen

Publisher

Hindawi Limited

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)