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Improved blur kernel estimation with blurred and noisy image pairs

In this paper, we propose a TV-L1 denoising model-based 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.