Abstract
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 convolution kernels with different sizes to expand the receptive field in parallel. (3) The ablation experiments verify the effectiveness of each employed loss measurement in our devised loss function. And our experimental studies demonstrate that the proposed model can effectively expand the receptive field and thus reconstruct images with high resolution and accuracy and that the proposed MFGAN method performs better than a few state-of-the-art methods.
Highlights
As images can carry a great deal of data and information, image processing technology is applicable to many fields, such as medical treatment, transportation, military, space and aerospace, and communication engineering
(2) Motivated by the Inception structure and introducing a multiscale-fusion strategy, our method is capable of using the multiple convolution kernels with different sizes to expand the receptive field in parallel. (3) e ablation experiments verify the effectiveness of each employed loss measurement in our devised loss function
Our experimental studies demonstrate that the proposed model can effectively expand the receptive field and reconstruct images with high resolution and accuracy and that the proposed multiscale-fusion GAN (MFGAN) method performs better than a few state-of-the-art methods
Summary
As images can carry a great deal of data and information, image processing technology is applicable to many fields, such as medical treatment, transportation, military, space and aerospace, and communication engineering. It has penetrated into our lives and been inseparable from each of us. Deep learning is widely adopted to solve image denoising problems, where the main methods are multilayer perceptrons and fully convolutional networks. Generative Adversarial Network (GAN) has nowadays attracted much attention from researchers Various network frameworks, such as deep convolutional GAN (DCGAN), CycleGAN [11], and Pix2pix, have been proposed based on GAN for image processing
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