Robust penalty-weighted deblurring via kernel adaption using single image |
| |
| Affiliation: | 1. School of Information Engineering, Guangdong University of Technology, PR China;2. Fujian Provincial Key Laboratory of Data Mining and Applications, Fujian University of Technology, Fujian, PR China;1. Maulana Azad National Institute of Technology, Bhopal, MP, India;2. Junction Software Pvt. Ltd., Bhopal, MP, India;1. Department of Computer Science, Jinan University, Guangzhou, China;2. Nanjing University of Information Science & Technology, Nanjing, China;3. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China;4. Guangdong Provincial Big Data Collaborative Innovation Center, Shenzhen University, Shenzhen, China;1. College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China;2. Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan |
| |
| Abstract: | Image blind deconvolution is well known as a challenging, ill-posed problem due to the uncertainty of the blur kernel and the noise condition. Based on our observations, blind deconvolution algorithms tend to generate disconnected and noisy blur kernels, which would yield a serious ringing effect in the restored image if the input image is noisy. Therefore, there is still room for further improvement, especially for noisy images captured under poor illumination conditions. In this paper, we propose a robust blind deconvolution algorithm by adopting a penalty-weighted anisotropic diffusion prior. On one hand, the anisotropic diffusion prior effectively eliminates the discontinuity in the blur kernel caused by the noisy input image during the process of kernel estimation. On the other hand, the weighted penalizer reduces the speckle noise of the blur kernel, thus improving the quality of the restored image. The effectiveness of the proposed algorithm is verified by both synthetic and real images with defocused or motion blur. |
| |
| Keywords: | Blind deconvolution Motion blur Defocus blur Regularization Split Bregman |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
