blind restoration
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2021 ◽  
Vol 1 (1) ◽  
pp. 25-32
Author(s):  
Meryem H. Muhson ◽  
Ayad A. Al-Ani

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.  


2021 ◽  
Author(s):  
Basma Ahmed ◽  
Mohamed Abdel-Nasser ◽  
Osama A. Omer ◽  
Amal Rashed ◽  
Domenec Puig

Blind or non-referential image quality assessment (NR-IQA) indicates the problem of evaluating the visual quality of an image without any reference, Therefore, the need to develop a new measure that does not depend on the reference pristine image. This paper presents a NR-IQA method based on restoration scheme and a structural similarity index measure (SSIM). Specifically, we use blind restoration schemes for blurred images by reblurring the blurred image and then we use it as a reference image. Finally, we use the SSIM as a full reference metric. The experiments performed on standard test images as well as medical images. The results demonstrated that our results using a structural similarity index measure are better than other methods such as spectral kurtosis-based method.


Author(s):  
S. Wang ◽  
Q. Chen ◽  
C. He ◽  
C. Zhang ◽  
L. Zhong ◽  
...  

Author(s):  
Saiyan Wu ◽  
Hui Yang

In the paper, we proposed a new iterative algorithm and use a entirely new iterative factor. Firstly, we adopt the Exp function in the iterative factor, because we want each iterative result preserves the nonnegative constraint; Secondly, we make the iterative factor in a reciprocal form ,this way can produce two advantages, one is we can get a more stable and continuous results after each iteration; the other is we can achieve this algorithm in hardware more convenient. Thirdly, we add a low-pass filter and the edge of the scale in the iterative factor, this way we can get a better result, the image SNR is higher and the MSE is lower. Meanwhile for the image sequence, we adopt the two-step iterative algorithm. The result shows the algorithm own the faster convergence speed and the better convergence result. Different from the other algorithm for blind restoration, although we should select the parameter in the starting of the algorithm, the algorithm doesn’t sensitive for the parameter. So the algorithm possesses very strong adaptability for the blind image deblurring. So a novel algorithm based on an iterative and nonnegative algorithm was proposed to perform blind deconvolution.


2020 ◽  
Vol 10 (4) ◽  
pp. 809-813
Author(s):  
Ting Han ◽  
Ruo-Han Zhao ◽  
Mo Dong

In order to study the realization of medical image restoration, this study mainly adopts blind equalization algorithm to analyze medical images, and observes the improvement effect of blind equalization technology on medical images. In the process of medical image formation, it is unavoidable to be affected by point spread function, which leads to image degradation and brings great difficulties to diagnosis, and the results of degradation are often unpredictable. The results show that the blind restoration algorithm can restore the image when the degradation process of the medical image is uncertain, which makes the medical image clearer and more accurate, brings great convenience to the diagnosis, and also reduces the diagnostic errors caused by the unclear image.


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