Image degradation during remote photography severely affects high-resolution imaging and high-accuracy detection. To improve the quality of remote sensing images

a multi-scale image deblurring method for remote sensing viaregularization constraintsis proposed in this paper. At the beginning of deblurring

bilateral and shock filtersare used to handle blurred images.Subsequently

a variational Bayesian iterative model is applied to determine the optimal solution by considering prior knowledge of the sparsity feature of the blur kernel.Finally

the deblurring result can be obtained by non-blind deconvolution based on gradient sparsity. In addition

the effect of scale information on the deblurring result is studied for the case of a serious blur

and a multi-scale iterative method is proposed. Our algorithm is implemented for deblurring numerous remote sensing images. Experimental results show that the proposed method can effectively remove fuzzy sections

maintain edges

and recover details of blurred images. Other methods are compared with the proposed algorithm.Indices such as entropy

contrast ratio(CR)

edge strength level(ESL)

and HSV (hue

saturation

value)model are used in the objective evaluation. The ESL average of the images increases by 28.7%

where as the CR average increases by 17.6% after using our method. Subjective visual experience and objective evaluation indices show that the proposed method can effectively improve the quality of remote sensing images.