Accurate Kernel Estimation for Blind Deblurring of Noisy and Blurred Images

Most state-of-the-art single image blind deblurring techniques can’t still handle perfectly the problem that the quality of blur kernel estimate can be degraded dramatically when the input image noise can’t be ignored. In this work, we present a new method for estimating an accurate blur kernel from a blurry and noisy image using salient image structure. First, we use denoising as a preprocess to remove the input image noise, and then compute salient structure of the denoised result based on the total variation（TV） model. We also apply a gradient selection method to remove those salient edges that have a possible adverse effect on blur kernel estimation, thus improving the robustness of blur kernel estimation. Next, we adopt a two-phase blur kernel estimation strategy to achieve an accurate kernel estimation by taking advantage of the blur kernel estimation method from salient structure and iterative support detection（ISD） technique. Finally, we choose to use the non-blind deconvolution method with sparse prior knowledge to attain the final latent image restoration. Experiment results on synthetic and real world data show that our method produces more accurate blur kernels and higher quality latent images than previous approaches on noisy and blurry images. It handles effectively the truth that image deblurring techniques are very sensitive to noise, and estimates an accurate blur kernel from a noisy and blurry image.