Super-resolution restoration of degraded image based on fuzzy enhancement

A novel enhancement algorithm for degraded image using dual-domain-adaptive wavelet and improved fuzzy transform is proposed, aiming at the problem of surveillance videos degradation caused by the complex lighting conditions underground coal mine. Firstly, the dual-domain filtering (DDF) is used to decompose the image into base image and detail image, and the contrast limited adaptive histogram enhancement (CLAHE) is adopted to adjust the overall brightness and contrast of the base image. Then, the discrete wavelet transform (DWT) is utilized to obtain the low frequency sub-band (LFS) and high frequency sub-band (HFS). Next, the wavelet shrinkage threshold is applied to calculate the wavelet threshold corresponding to the HFS at different scales. Meanwhile, a new Garrate threshold function that introduces adjustment factor and enhancement coefficient is designed to adaptively de-noise and enhance the HFS coefficients, and the Gamma function is employed to correct the LFS coefficients. Finally, the PAL fuzzy enhancement operator is improved and used to perform contrast enhancement and highlight area suppression on the reconstructed image to obtain an enhanced image. Experimental results show that the proposed algorithm can not only significantly improve the overall brightness and contrast of the degraded image but also suppresses the noise of dust and spray and enhances the image details. Compared with the similar algorithms of STFE, GTFE, CLAHE, SSR, MSR, DGR, and MSWT algorithms, the indicator values of comprehensive performance of the proposed algorithm are increased by 205%, 195%, 200%, 185%, 185%, 85%, 140%, and 215%, respectively. Moreover, compared with the other seven algorithms, the proposed algorithm has strong robustness and is more suitable for image enhancement in different mine environments.

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An Image Fuzzy Enhancement Adopting Linear Membership Function

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.543-547.2213 ◽

2014 ◽

Vol 543-547 ◽

pp. 2213-2216

Author(s):

Dong Ming Zhang ◽

Li Jia Chen ◽

Wei Gao

Keyword(s):

Membership Function ◽

Low Contrast ◽

Fuzzy Enhancement ◽

Linear Scheme ◽

Degraded Image ◽

Level Information ◽

Image Edges ◽

Contrast Stretching ◽

Fuzzy Image ◽

Stretching Effect

S.K.Pal's fuzzy set theory has been used to deal with the degraded image, wherein the image edges are uncertain and inaccurate. But Pals algorithm losses low grey level information of the original image and the grayscales of the image can not be extended. A fuzzy image enhancement based on linear membership function is proposed through the analysis of classical Pal's fuzzy enhancement. This algorithm avoids the lost of low grades information of the image as well as increases the image's whole grey scales. It is very suited for low grades, low contrast images such as X-ray images. As a result of the linear transformation comparing with Pal's non-linear scheme, the image processing speed is also improved. Experiment results show that the proposed method outperforms traditional Pal's in terms of contrast stretching effect and speed.

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BLIND RESTORATION USING CONVOLUTION NEURAL NETWORK

Iraqi Journal of Information & Communications Technology ◽

10.31987/ijict.1.1.178 ◽

2021 ◽

Vol 1 (1) ◽

pp. 25-32

Author(s):

Meryem H. Muhson ◽

Ayad A. Al-Ani

Keyword(s):

Neural Network ◽

Image Restoration ◽

Super Resolution ◽

Degraded Image ◽

Blind Restoration ◽

High Resolution Images ◽

Image Super Resolution ◽

Single Image Super Resolution ◽

Blurred Images ◽

Degraded Images

Image restoration is a branch of image processing that involves a mathematical deterioration and restoration model to restore an original image from a degraded image. This research aims to restore blurred images that have been corrupted by a known or unknown degradation function. Image restoration approaches can be classified into 2 groups based on degradation feature knowledge: blind and non-blind techniques. In our research, we adopt the type of blind algorithm. A deep learning method (SR) has been proposed for single image super-resolution. This approach can directly learn an end-to-end mapping between low-resolution images and high-resolution images. The mapping is expressed by a deep convolutional neural network (CNN). The proposed restoration system must overcome and deal with the challenges that the degraded images have unknown kernel blur, to deblur degraded images as an estimation from original images with a minimum rate of error.

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Multi-task Learning-based All-in-one Collaboration Framework for Degraded Image Super-resolution

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3417333 ◽

2021 ◽

Vol 17 (1) ◽

pp. 1-21

Author(s):

Xin Jin ◽

Jianfeng Xu ◽

Kazuyuki Tasaka ◽

Zhibo Chen

Keyword(s):

High Resolution ◽

Super Resolution ◽

Input Image ◽

Motion Blur ◽

Estimation Accuracy ◽

Low Resolution ◽

Task Learning ◽

Degraded Image ◽

Image Super Resolution ◽

Low Resolution Images

In this article, we address the degraded image super-resolution problem in a multi-task learning (MTL) manner. To better share representations between multiple tasks, we propose an all-in-one collaboration framework (ACF) with a learnable “junction” unit to handle two major problems that exist in MTL—“How to share” and “How much to share.” Specifically, ACF consists of a sharing phase and a reconstruction phase. Considering the intrinsic characteristic of multiple image degradations, we propose to first deal with the compression artifact, motion blur, and spatial structure information of the input image in parallel under a three-branch architecture in the sharing phase. Subsequently, in the reconstruction phase, we up-sample the previous features for high-resolution image reconstruction with a channel-wise and spatial attention mechanism. To coordinate two phases, we introduce a learnable “junction” unit with a dual-voting mechanism to selectively filter or preserve shared feature representations that come from sharing phase, learning an optimal combination for the following reconstruction phase. Finally, a curriculum learning-based training scheme is further proposed to improve the convergence of the whole framework. Extensive experimental results on synthetic and real-world low-resolution images show that the proposed all-in-one collaboration framework not only produces favorable high-resolution results while removing serious degradation, but also has high computational efficiency, outperforming state-of-the-art methods. We also have applied ACF to some image-quality sensitive practical task, such as pose estimation, to improve estimation accuracy of low-resolution images.

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