scholarly journals Spectral Norm Regularization for Blind Image Deblurring

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1856
Author(s):  
Shuhan Sun ◽  
Zhiyong Xu ◽  
Jianlin Zhang
Keyword(s):  
Degradation Process ◽  
Image Deblurring ◽  
Spectral Norm ◽  
Blind Image Deblurring ◽  
Blind Deblurring ◽  
Image Regularization ◽  
Symmetrical Form ◽  
Ill Posed ◽  
Degraded Images ◽  
Blind Image

Blind image deblurring is a well-known ill-posed inverse problem in the computer vision field. To make the problem well-posed, this paper puts forward a plain but effective regularization method, namely spectral norm regularization (SN), which can be regarded as the symmetrical form of the spectral norm. This work is inspired by the observation that the SN value increases after the image is blurred. Based on this observation, a blind deblurring algorithm (BDA-SN) is designed. BDA-SN builds a deblurring estimator for the image degradation process by investigating the inherent properties of SN and an image gradient. Compared with previous image regularization methods, SN shows more vital abilities to differentiate clear and degraded images. Therefore, the SN of an image can effectively help image deblurring in various scenes, such as text, face, natural, and saturated images. Qualitative and quantitative experimental evaluations demonstrate that BDA-SN can achieve favorable performances on actual and simulated images, with the average PSNR reaching 31.41, especially on the benchmark dataset of Levin et al.

scholarly journals Blind Deblurring Based on Sigmoid Function

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3484
Author(s):  
Shuhan Sun ◽  
Lizhen Duan ◽  
Zhiyong Xu ◽  
Jianlin Zhang
Keyword(s):  
Performance Optimization ◽  
Degradation Process ◽  
Image Deconvolution ◽  
Sigmoid Function ◽  
Blind Image Deblurring ◽  
Linear Estimators ◽  
Blind Deblurring ◽  
Degraded Image ◽  
Excellent Property ◽  
Blind Image

Blind image deblurring, also known as blind image deconvolution, is a long-standing challenge in the field of image processing and low-level vision. To restore a clear version of a severely degraded image, this paper proposes a blind deblurring algorithm based on the sigmoid function, which constructs novel blind deblurring estimators for both the original image and the degradation process by exploring the excellent property of sigmoid function and considering image derivative constraints. Owing to these symmetric and non-linear estimators of low computation complexity, high-quality images can be obtained by the algorithm. The algorithm is also extended to image sequences. The sigmoid function enables the proposed algorithm to achieve state-of-the-art performance in various scenarios, including natural, text, face, and low-illumination images. Furthermore, the method can be extended naturally to non-uniform deblurring. Quantitative and qualitative experimental evaluations indicate that the algorithm can remove the blur effect and improve the image quality of actual and simulated images. Finally, the use of sigmoid function provides a new approach to algorithm performance optimization in the field of image restoration.

scholarly journals Blind UAV Images Deblurring Based on Discriminative Networks

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2874 ◽  
Author(s):  
Ruihua Wang ◽  
Guorui Ma ◽  
Qianqing Qin ◽  
Qiang Shi ◽  
Juntao Huang
Keyword(s):  
Optical Axis ◽  
Image Plane ◽  
Image Deblurring ◽  
Imaging Systems ◽  
Blind Image Deblurring ◽  
Discriminative Model ◽  
Ill Posed ◽  
Uav Images ◽  
Uav Image ◽  
Blind Image

Unmanned aerial vehicles (UAVs) have become an important technology for acquiring high-resolution remote sensing images. Because most space optical imaging systems of UAVs work in environments affected by vibrations, the optical axis motion and image plane jitter caused by these vibrations easily result in blurring of UAV images. In the paper; we propose an advanced UAV image deblurring method based on a discriminative model comprising a classifier for blurred and sharp UAV images which is embedded into the maximum a posteriori framework as a regularization term that constantly optimizes ill-posed problem of blind image deblurring to obtain sharper UAV images. Compared with other methods, the results show that in image deblurring experiments using both simulated and real UAV images the proposed method delivers sharper images of various ground objects.

scholarly journals Dual Image Deblurring Using Deep Image Prior

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2045
Author(s):  
Chang Jong Shin ◽  
Tae Bok Lee ◽  
Yong Seok Heo
Keyword(s):  
Image Deblurring ◽  
Superior Performance ◽  
Natural Image ◽  
Blind Image Deblurring ◽  
Image Prior ◽  
Ill Posed ◽  
Deep Image ◽  
Sharp Image ◽  
Blurred Images ◽  
Blind Image

Blind image deblurring, one of the main problems in image restoration, is a challenging, ill-posed problem. Hence, it is important to design a prior to solve it. Recently, deep image prior (DIP) has shown that convolutional neural networks (CNNs) can be a powerful prior for a single natural image. Previous DIP-based deblurring methods exploited CNNs as a prior when solving the blind deburring problem and performed remarkably well. However, these methods do not completely utilize the given multiple blurry images, and have limitations of performance for severely blurred images. This is because their architectures are strictly designed to utilize a single image. In this paper, we propose a method called DualDeblur, which uses dual blurry images to generate a single sharp image. DualDeblur jointly utilizes the complementary information of multiple blurry images to capture image statistics for a single sharp image. Additionally, we propose an adaptive L2_SSIM loss that enhances both pixel accuracy and structural properties. Extensive experiments show the superior performance of our method to previous methods in both qualitative and quantitative evaluations.

Sparsity and Smoothing Multi-Regularization Constraints for Blind Image Deblurring

2013 ◽  
Vol 24 (5) ◽  
pp. 1143-1154 ◽  
Author(s):  
Shu TANG ◽  
Wei-Guo GONG ◽  
Jian-Hua ZHONG
Keyword(s):  
Image Deblurring ◽  
Blind Image Deblurring ◽  
Blind Image

Blind image deblurring using group sparse representation

2020 ◽  
Vol 102 ◽  
pp. 102736 ◽  
Author(s):  
Zhenhua Xu ◽  
Huasong Chen ◽  
Zhenhua Li
Keyword(s):  
Sparse Representation ◽  
Image Deblurring ◽  
Blind Image Deblurring ◽  
Blind Image

scholarly journals Blind Image Deblurring Based on Local Edges Selection

2019 ◽  
Vol 9 (16) ◽  
pp. 3274
Author(s):  
Han ◽  
Kan
Keyword(s):  
Image Deblurring ◽  
Color Images ◽  
Clear Image ◽  
Blind Image Deblurring ◽  
Alternating Direction ◽  
Dark Channel ◽  
The Difference ◽  
And Performance ◽  
Image Edges ◽  
Blind Image

The edges of images are less sparse when images become blurred. Selecting effective image edges is a vital step in image deblurring, which can help us to build image deblurring models more accurately. While global edges selection methods tend to fail in capturing dense image structures, the edges are easy to be affected by noise and blur. In this paper, we propose an image deblurring method based on local edges selection. The local edges are selected by the difference between the bright channel and the dark channel. Then a novel image deblurring model including local edges regularization term is established. The obtaining of a clear image and blurring kernel is based on alternating iterations, in which the clear image is obtained by the alternating direction method of multipliers (ADMM). In the experiments, tests are carried out on gray value images, synthetic color images and natural color images. Compared with other state-of-the-art blind image deblurring methods, the visualization results and performance verify the effectiveness of our method.

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