A Nonblind Deconvolution Method by Bias Correction for Inaccurate Blur Kernel Estimation in Image Deblurring

The blur kernel estimated by a blind deblurring algorithm is hardly to be error-free. The blur kernel error is usually ignored in the nonblind deconvolution stage and may result in severe artifacts or other negative effects. In addition, the bias hidden in the blurry image formation model has not been found and investigated due to ignoring the existence of the blur kernel errors. To this end, we develop a nonblind deconvolution method by bias correction for inaccurate blur kernels in this article, which are constructed on the basis of the classic errors-in-variables (EIVs) model. First we analyze in detail the bias caused by errors of inaccurate blur kernel from blurry image formation model. Next, the latent sparsity property of jointed latent image and errors of inaccurate blur kernel is counted statistically, which is imposed as a new regularization term. Then, the objective function of the new nonblind method is established, and an alternative minimization algorithm is derived and employed to estimate the latent clear image. Furthermore, a filtering method is utilized to modify the bias term, which is added to amend intermediate image in each iteration. Finally, extensive experiments with benchmark datasets and blurred micro-nano satellite remote sensing images are carried out to evaluate the proposed method. Experimental results demonstrate that the proposed method can obtain high-quality restored images, and it is comparable to or even better than some state-of-the-art nonblind deconvolution methods.