Improved Blur Kernel Estimation with Blurred and Noisy Image Pairs
In this paper, we propose a TV-L denoising modelbased kernel estimation in image deblurring which uses both blurred and noisy images. More details and edges are recovered in the denoised image which is used to replace the true image and do the deconvolution. In the first instance, an initial kernel which might be very noisy can be recovered after primary kernel estimation. Whereafter, the method of hysteresis thresholding by using a mask is used to suppress the noise and finally an accurate estimated kernel can be obtained. Experimental results show that our outcome is significantly evolutional.
TV-L1 denoising model kernel estimation hysteresis thresholding
Qian Wan Yuan-Biao Zhan Chuan He Jia-Di Wan
Department of Computer Science and Technology Packaging Engineering Institute Mathematical Modeling Innovative Practice Base, Zhuhai Campus of Jin
国际会议
成都
英文
10-12
2010-06-12（万方平台首次上网日期，不代表论文的发表时间）