Variable Exponent Regularization Approach for Blur Kernel Estimation of Remote Sensing Image Blind Restoration

Spatial remote sensing images are usually degraded during image capturing procedures mainly due to the mixed factors of atmospheric turbulence, spacecraft motion, and out of focus lenses. The real point spread function (PSF) of the whole imaging system is the convolution of all factors contributing to degradation. The exact degradation PSF model estimation is important for the image restoration result. In this paper, we considered the properties of the mixed degradation factors and proposed a new blind deconvolution model to simultaneously estimate and remove blurs from remote sensing images. Inconsistent with existing models, which mainly focus on only one degradation type and estimate blur kernel parameters using the fixed regularizer, we concentrated on the diversity of different PSF types and used the variable exponent regularizer to improve kernel flexibility. The proposed model could estimate not only single PSF types, such as motion, uniform, and Gaussian, but also composite PSFs of different types. Following the split Bregman method, we employed an efficient computational method, which did not require PSF initial values, to minimize the proposed cost function iteratively. Experimental results demonstrated the effectiveness and robustness of the proposed method for simulated and real remote sensing images with different PSFs' types.