Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Blind image deblurring is a severely ill-posed inverse problem. To obtain a high quality latent image from a single blurred one, effective regularizations are required. ,  propose a nonlocal regularization to improve blur kernel estimation. Under convolution operation, even similar patches could result in the quite different values. Hover, if the estimated kernel is correct, the nonlocal similar patches ighted by that kernel may result in the similar value by convolution. Therefore, the ighted nonlocal patches can improve the kernel estimation.  extract the nonlocal patches in terms of the ighted similarity by the kernel and then use them for regularization of the kernel estimation. Since the nonlocal regularization is a data-authentic prior, our approach not only mitigates the ill-posedness but also imposes the effective prior to kernel estimation. Experimental results show that our approach outperforms conventional blind deblurring algorithms.
['Suil Son', 'Suk I. Yoo']
I am writing a research paper on a idea Blur kernel estimation via salient edges and nonlocal regularization
[{"from": "human", "value": "I am writing a research paper on a idea Blur kernel estimation via salient edges and nonlocal regularization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Blind image deblurring is a severely ill-posed inverse problem. To obtain a high quality latent image from a single blurred one, effective regularizations are required. ,  propose a nonlocal regularization to improve blur kernel estimation. Under convolution operation, even similar patches could result in the quite different values. Hover, if the estimated kernel is correct, the nonlocal similar patches ighted by that kernel may result in the similar value by convolution. Therefore, the ighted nonlocal patches can improve the kernel estimation.  extract the nonlocal patches in terms of the ighted similarity by the kernel and then use them for regularization of the kernel estimation. Since the nonlocal regularization is a data-authentic prior, our approach not only mitigates the ill-posedness but also imposes the effective prior to kernel estimation. Experimental results show that our approach outperforms conventional blind deblurring algorithms."}]