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Yi Li, Nan Wang, Jinlong Li and Yu Zhang
Although the existing deblurring methods for defocused images are capable of approximately recovering clear images, they still exhibit certain limitations, such as ringing artifacts and remaining blur. Along these lines, in this work, a novel deep-learni...
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Yi Wang, Yating Xu, Tianjian Li, Tao Zhang and Jian Zou
Image deblurring based on sparse regularization has garnered significant attention, but there are still certain limitations that need to be addressed. For instance, convex sparse regularization tends to exhibit biased estimation, which can adversely impa...
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Soon Hock Ng, Vijayakumar Anand, Molong Han, Daniel Smith, Jovan Maksimovic, Tomas Katkus, Annaleise Klein, Keith Bambery, Mark J. Tobin, Jitraporn Vongsvivut and Saulius Juodkazis
The Fourier transform infrared microspectroscopy (FTIRm) system of the Australian Synchrotron has a unique optical configuration with a peculiar beam profile consisting of two parallel lines. The beam is tightly focused using a 36× Schwarzschild objectiv...
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Zhen Ye, Xiaoming Ou, Juhua Huang and Yingpin Chen
A traditional total variation (TV) model for infrared image deblurring amid salt-and-pepper noise produces a severe staircase effect. A TV model with low-order overlapping group sparsity (LOGS) suppresses this effect; however, it considers only the prior...
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Shunlei Li, Muhammad Adeel Azam, Ajay Gunalan and Leonardo S. Mattos
Optical coherence tomography (OCT) is a rapidly evolving imaging technology that combines a broadband and low-coherence light source with interferometry and signal processing to produce high-resolution images of living tissues. However, the speckle noise...
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Lijia Yu, Jie Luo, Shaoping Xu, Xiaojun Chen and Nan Xiao
Image denoising is a classic but still important issue in image processing as the denoising effect has a significant impact on subsequent image processing results, such as target recognition and edge detection. In the past few decades, various denoising ...
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Xiaobin Yuan, Jingping Zhu and Xiaobin Li
Blind image deblurring tries to recover a sharp version from a blurred image, where blur kernel is usually unknown. Recently, sparse representation has been successfully applied to estimate the blur kernel. However, the sparse representation has not cons...
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Minsoo Hong and Yoonsik Choe
The de-blurring of blurred images is one of the most important image processing methods and it can be used for the preprocessing step in many multimedia and computer vision applications. Recently, de-blurring methods have been performed by neural network...
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Fan Lin, Yingpin Chen, Yuqun Chen and Fei Yu
Image deblurring under the background of impulse noise is a typically ill-posed inverse problem which attracted great attention in the fields of image processing and computer vision. The fast total variation deconvolution (FTVd) algorithm proved to be an...
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Mike Giansiracusa,Larry Pearlstein,Tyler Daws,Soundararajan Ezekiel,Abdullah Ali Alshehri
Multi-resolution image decomposition transforms are a popular approach to current image processing problems such as image fusion, noise reduction, and deblurring. Over the past few decades, new algorithms have been developed based on the wavelet transfor...
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