scholarly journals Blind Image Deconvolution Algorithm Based on Sparse Optimization with an Adaptive Blur Kernel Estimation

2020 ◽  
Vol 10 (7) ◽  
pp. 2437 ◽  
Author(s):  
Haoyuan Yang ◽  
Xiuqin Su ◽  
Songmao Chen
Keyword(s):  
Blind Deconvolution ◽  
Imaging System ◽  
Real Life ◽  
Kernel Estimation ◽  
Input Image ◽  
Motion Blur ◽  
Sparse Optimization ◽  
Image Deconvolution ◽  
Deconvolution Algorithm ◽  
Blur Kernel

Image blurs are a major source of degradation in an imaging system. There are various blur types, such as motion blur and defocus blur, which reduce image quality significantly. Therefore, it is essential to develop methods for recovering approximated latent images from blurry ones to increase the performance of the imaging system. In this paper, an image blur removal technique based on sparse optimization is proposed. Most existing methods use different image priors to estimate the blur kernel but are unable to fully exploit local image information. The proposed method adopts an image prior based on nonzero measurement in the image gradient domain and introduces an analytical solution, which converges quickly without additional searching iterations during the optimization. First, a blur kernel is accurately estimated from a single input image with an alternating scheme and a half-quadratic optimization algorithm. Subsequently, the latent sharp image is revealed by a non-blind deconvolution algorithm with the hyper-Laplacian distribution-based priors. Additionally, we analyze and discuss its solutions for different prior parameters. According to the tests we conducted, our method outperforms similar methods and could be suitable for dealing with image blurs in real-life applications.

scholarly journals Motion Blur Kernel Estimation via Deep Learning

2018 ◽  
Vol 27 (1) ◽  
pp. 194-205 ◽  
Author(s):  
Xiangyu Xu ◽  
Jinshan Pan ◽  
Yu-Jin Zhang ◽  
Ming-Hsuan Yang
Keyword(s):  
Deep Learning ◽  
Kernel Estimation ◽  
Motion Blur ◽  
Blur Kernel Estimation ◽  
Blur Kernel

scholarly journals Robust Motion Blur Kernel Estimation by Kernel Continuity Prior

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 46162-46175
Author(s):  
Xueling Chen ◽  
Yu Zhu ◽  
Jinqiu Sun ◽  
Yanning Zhang
Keyword(s):  
Kernel Estimation ◽  
Motion Blur ◽  
Blur Kernel Estimation ◽  
Blur Kernel

scholarly journals Iterative-Trained Semi-Blind Deconvolution Algorithm to Compensate Straylight in Retinal Images

2021 ◽  
Vol 7 (4) ◽  
pp. 73
Author(s):  
Francisco J. Ávila ◽  
Jorge Ares ◽  
María C. Marcellán ◽  
María V. Collados ◽  
Laura Remón
Keyword(s):  
Healthy Subjects ◽  
Spatial Information ◽  
Blind Deconvolution ◽  
Imaging System ◽  
Retinal Imaging ◽  
Optical Quality ◽  
Retinal Images ◽  
Image Sharpness ◽  
Deconvolution Algorithm ◽  
Spread Function

The optical quality of an image depends on both the optical properties of the imaging system and the physical properties of the medium in which the light travels from the object to the final imaging sensor. The analysis of the point spread function of the optical system is an objective way to quantify the image degradation. In retinal imaging, the presence of corneal or cristalline lens opacifications spread the light at wide angular distributions. If the mathematical operator that degrades the image is known, the image can be restored through deconvolution methods. In the particular case of retinal imaging, this operator may be unknown (or partially) due to the presence of cataracts, corneal edema, or vitreous opacification. In those cases, blind deconvolution theory provides useful results to restore important spatial information of the image. In this work, a new semi-blind deconvolution method has been developed by training an iterative process with the Glare Spread Function kernel based on the Richardson-Lucy deconvolution algorithm to compensate a veiling glare effect in retinal images due to intraocular straylight. The method was first tested with simulated retinal images generated from a straylight eye model and applied to a real retinal image dataset composed of healthy subjects and patients with glaucoma and diabetic retinopathy. Results showed the capacity of the algorithm to detect and compensate the veiling glare degradation and improving the image sharpness up to 1000% in the case of healthy subjects and up to 700% in the pathological retinal images. This image quality improvement allows performing image segmentation processing with restored hidden spatial information after deconvolution.

scholarly journals Super Resolution with Kernel Estimation and Dual Attention Mechanism

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 508
Author(s):  
Huan Liang ◽  
Youdong Ding ◽  
Fei Wang ◽  
Yuzhen Gao ◽  
Xiaofeng Qiu
Keyword(s):  
Kernel Estimation ◽  
Simulated Data ◽  
Super Resolution ◽  
Input Image ◽  
Attention Mechanism ◽  
High Quality ◽  
Blur Kernel Estimation ◽  
Internal Relations ◽  
Blur Kernel ◽  
Adaptively Adjusting

Convolutional Neural Networks (CNN) have led to promising performance in super-resolution (SR). Most SR methods are trained and evaluated on predefined blur kernel datasets (e.g., bicubic). However, the blur kernel of real-world LR image is much more complex. Therefore, the SR model trained on simulated data becomes less effective when applied to real scenarios. In this paper, we propose a novel super resolution framework based on blur kernel estimation and dual attention mechanism. Our network learns the internal relations from the input image itself, thus the network can quickly adapt to any input image. We add the blur kernel estimation structure into the network, correcting the inaccurate blur kernel to generate high quality images. Meanwhile, we propose a dual attention mechanism to restore the texture details of the image, adaptively adjusting the features of the image by considering the interdependencies both in channel and spatial. The combination of blur kernel estimation and attention mechanism makes our network perform well for complex blur images in practice. Extensive experiments show that our method (KASR) achieves promising accuracy and visual improvements against most existing methods.

A self-adaptive learning method for motion blur kernel estimation of the single image

Optik ◽  
2021 ◽  
Vol 248 ◽  
pp. 168023
Author(s):  
Wei Zhou ◽  
Xingxing Hao ◽  
Jin Cui ◽  
Yongxiang Yu ◽  
Xin Cao ◽  
...  
Keyword(s):  
Adaptive Learning ◽  
Kernel Estimation ◽  
Motion Blur ◽  
Learning Method ◽  
Single Image ◽  
Blur Kernel Estimation ◽  
Blur Kernel ◽  
Self Adaptive

scholarly journals Blind Deblurring of Saturated Images Based on Optimization and Deep Learning for Dynamic Visual Inspection on the Assembly Line

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 678 ◽  
Author(s):  
Bodi Wang ◽  
Guixiong Liu ◽  
Junfang Wu
Keyword(s):  
Deep Learning ◽  
Visual Inspection ◽  
Kernel Estimation ◽  
Visual Quality ◽  
Motion Blur ◽  
Blind Deblurring ◽  
Blur Kernel Estimation ◽  
Wide Range ◽  
Good Score ◽  
Blur Kernel

Image deblurring can improve visual quality and mitigates motion blur for dynamic visual inspection. We propose a method to deblur saturated images for dynamic visual inspection by applying blur kernel estimation and deconvolution modeling. The blur kernel is estimated in a transform domain, whereas the deconvolution model is decoupled into deblurring and denoising stages via variable splitting. Deblurring predicts the mask specifying saturated pixels, which are then discarded, and denoising is learned via the fast and flexible denoising network (FFDNet) convolutional neural network (CNN) at a wide range of noise levels. Hence, the proposed deconvolution model provides the benefits of both model optimization and deep learning. Experiments demonstrate that the proposed method suitably restores visual quality and outperforms existing approaches with good score improvements.

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