scholarly journals Restoration of Partial Blurred Image Based on Blur Detection and Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Dong Yang ◽  
Shiyin Qin
Keyword(s):  
Kernel Estimation ◽  
Image Deblurring ◽  
Detection Algorithm ◽  
Classification Algorithm ◽  
Estimation Methods ◽  
Blur Detection ◽  
Simulated Experiment ◽  
Blur Kernel Estimation ◽  
Restoration Algorithm ◽  
Blurred Image

A new restoration algorithm for partial blurred image which is based on blur detection and classification is proposed in this paper. Firstly, a new blur detection algorithm is proposed to detect the blurred regions in the partial blurred image. Then, a new blur classification algorithm is proposed to classify the blurred regions. Once the blur class of the blurred regions is confirmed, the structure of the blur kernels of the blurred regions is confirmed. Then, the blur kernel estimation methods are adopted to estimate the blur kernels. In the end, the blurred regions are restored using nonblind image deblurring algorithm and replace the blurred regions in the partial blurred image with the restored regions. The simulated experiment shows that the proposed algorithm performs well.

scholarly journals Obscure Image Classification and Restoration using Support Vector Machines

2019 ◽  
Vol 8 (4) ◽  
pp. 8231-8236
Keyword(s):  
Kernel Estimation ◽  
Classification Algorithm ◽  
Support Vector ◽  
Svm Classification ◽  
Vector Machines ◽  
Blurred Image ◽  
Simulation Results ◽  
Blurred Images ◽  
Identification And Characterization ◽  
Restoration Model

A restoration and classification computation for blurred image which depends on obscure identification and characterization is proposed in this paper. Initially, new obscure location calculation is proposed to recognize the Gaussian, Motion and Defocus based blurred locales in the image. The degradation-restoration model referred with pre-processing followed by binarization and features extraction/classification algorithm applied on obscure images. At this point, support vector machine (SVM) classification algorithm is proposed to cluster the blurred images. Once the obscure class of the locales is affirmed, the structure of the obscure kernels of the blurred images are affirmed. At that point, the obscure kernel estimation techniques are embraced to appraise the obscure kernels. At last, the blurred locales are re-established utilizing nonblind image deblurring calculation and supplant the blurred images with the restored images. The simulation results demonstrate that the proposed calculation performs well

scholarly journals Blur kernel estimation of noisy-blurred image via dynamic structure prior

2020 ◽  
Vol 403 ◽  
pp. 268-281
Author(s):  
Xueling Chen ◽  
Yu Zhu ◽  
Wei Liu ◽  
Jinqiu Sun ◽  
Yanning Zhang
Keyword(s):  
Kernel Estimation ◽  
Dynamic Structure ◽  
Blur Kernel Estimation ◽  
Blurred Image ◽  
Blur Kernel

Spatial-scale-regularized blur kernel estimation for blind image deblurring

2018 ◽  
Vol 68 ◽  
pp. 138-154 ◽  
Author(s):  
Shu Tang ◽  
Xianzhong Xie ◽  
Ming Xia ◽  
Lei Luo ◽  
Peisong Liu ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Kernel Estimation ◽  
Image Deblurring ◽  
Blind Image Deblurring ◽  
Blur Kernel Estimation ◽  
Blur Kernel ◽  
Blind Image

scholarly journals An Efficient Blind Image Deblurring Using a Smoothing Function

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Kittiya Khongkraphan ◽  
Aniruth Phonon ◽  
Sainuddeen Nuiphom
Keyword(s):  
Splitting Method ◽  
Closed Form Solution ◽  
Kernel Estimation ◽  
Image Deblurring ◽  
Form Solution ◽  
Smoothing Function ◽  
Image Method ◽  
Latent Image ◽  
Blind Image Deblurring ◽  
Blurred Image

This paper introduces an efficient deblurring image method based on a convolution-based and an iterative concept. Our method does not require specific conditions on images, so it can be widely applied for unspecific generic images. The kernel estimation is firstly performed and then will be used to estimate a latent image in each iteration. The final deblurred image is obtained from the convolution of the blurred image with the final estimated kernel. However, image deblurring is an ill-posed problem due to the nonuniqueness of solutions. Therefore, we propose a smoothing function, unlike previous approaches that applied piecewise functions on estimating a latent image. In our approach, we employ L2-regularization on intensity and gradient prior to converging to a solution of the deblurring problem. Moreover, our work is based on the quadratic splitting method. It guarantees that each subproblem has a closed-form solution. Various experiments on synthesized and real-world images confirm that our approach outperforms several existing methods, especially on the images corrupted by noises. Moreover, our method gives more reasonable and more natural deblurred images than those of other methods.

Sign in / Sign up

Export Citation Format

Share Document