Improving the quality of remote sensing images using a universal reconstruction method
Huanfeng Shen, Tinghua Ai, Pingxiang Li, Yi Wang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
This paper presents a universal maximum a posteriori (MAP) based reconstruction method which can be used for destriping, inpainting (the removal of dead pixels) and super resolution reconstruction (the recovery of a high resolution image from several low resolution images). In the MAP framework, the likelihood probability density function (PDF) is constructed based on a linear image observation model, and a robust Huber-Markov model is used as the prior PDF. A gradient descent optimization method is employed to produce the desired image. The proposed algorithm has been tested using MODIS images for destriping and super resolution reconstruction, and CBERS (China-Brazil Earth Resource Satellite) and QuickBird images for simulated inpainting. The experiment results and quantitative analyses verify the efficacy of this algorithm.