Destriping and Inpainting of Remote Sensing Images Using Maximum A-posteriori Method

In a large number of spaceborne and airborne multi-detector spectrometer imagery, there commonly exist image stripes and random dead pixels. The techniques to recover the image from the contaminated one are called image destriping (for stripes) and image inpainting (for dead pixels). In order to constrain the solution space according to a priori knowledge, this paper presents a maximum a posteriori (MAP) based algorithm for both destriping and inpainting problems. 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 on images of different sensors. Experimental results show that it performs quite well in terms of both quantitative measurements and visual evaluation.