MTF compensation algorithm based on blind deconvolution for high-resolution remote sensing satellite

In high resolution remote sensing satellite imaging system, image restoration is an important step to visualize ne details and mitigate the noise. The raw image data often presents poor imaging quality due to various reasons and Point Spread Function (PSF) measures such blurriness characteristic of the image using point source. Satellite image from Korea Multi-purpose Satellite 2 (KOMPSAT-2) also requires Modular Transfer Function (MTF) compensation process to achieve more realistic image which entails removing ringing artifacts at the edges and restraining excess use of denoising eect in order to keep it more realistic. This paper focuses on the deconvolution of KOMPSAT-2 image utilizing PSF attained from Korea Aerospace Research Institute compared to deconvolution with the estimated PSF blur kernel. The deconvolution algorithm considered are Richard-Lucy, Damped Richard-Lucy, Bilateral Richard-Lucy and Sparse Prior deconvolution algorithms.