Optical satellite image MTF compensation for remote-sensing data production

Modulation Transfer Function (MTF) compensation had been widely used in Remote-Sensing (RS) imagery processing for texture feature enhancement and high-frequency details restoration. In this paper, we propose an optical satellite MTF Compensation (MTFC) method for engineering applications and RS data production. The framework mainly includes Point Spread Function (PSF) model estimation and non-blind image deconvolution. In PSF estimation, we used the iterative total variation algorithm and 2D Gaussian model fitting to reduce the dependence on slant-edge quality and improve the efficiency of PSF measurement. The PSF model estimation error is less than 3% using the results from standard commercial software as an evaluation benchmark. In image deconvolution, we adopted a regularisation-based non-blind deconvolution method using hyper-Laplacian prior. Compared with the widely used methods, the MTF has been improved more than double in high-frequency part. Its processing efficiency and image quality can meet the requirements of RS image products. These results can effectively prove the engineering application value of our proposed method.