Abstract
Super resolution reconstruction is to produce one or a set of high resolution images from a sequence of low resolution images using the additional information among them. Traditional super resolution reconstruction algorithms are limited to their assumed data and noise model. The robust reconstruction algorithm which is not sensitive to model error has always been a hot research. We propose an alternate approach based on p-norm minimization and robust regularization with bilateral total variation (BTV). This method is robust to errors caused by motion and blur estimation. Hybrid steepest descent and limited storage quasi-Newton method is used to solve the cost function. Experiments are carried out in simulated images and ASTER multi-band thermal infrared images, experiment results indicate that the proposed method removes noises effectively and results in fine detail, sharp edge and rich content.
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