An improved richardson-lucy algorithm based on genetic approach for satellite image restoration

In the process of satellite imaging, the observed image is blurred by optical system and atmospheric effects and corrupted by additive noise. The deconvolution of blurred and noisy satellite images is an ill-posed inverse problem. In the literature, a number of image restoration methods have been proposed to reconstruct an approximated version of the original image from a degraded observation. The iterative method known as Richardson-Lucy deconvolution has demonstrated its effectiveness to compensate for these degradations. The efficiency of this method obviously depends on the iteration count that has a direct impact on the expected result. This decisive and virtually unknown parameter leads to the estimation of approximate values which may affect the quality of the restored image. In this paper, the idea consists of optimizing the iteration count of the Richardson-Lucy deconvolution by applying the genetic approach in order to get a better restoration of the degraded satellite image