Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Practical Optimization: Algorithms and Engineering Applications
Practical Optimization: Algorithms and Engineering Applications
Analysis of multiframe super-resolution reconstruction for image anti-aliasing and deblurring
Image and Vision Computing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
Multiframe demosaicing and super-resolution of color images
IEEE Transactions on Image Processing
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Multiframe image super-resolution algorithms can be used to obtain a higher-resolution higher-quality image from a set of low-resolution, blurred, and noisy images. Very often, these algorithms rely on an optimization-based inversion of the image acquisition model. Recently, two algorithms for grayscale and hybrid demosaicing and color super-resolution have been proposed by Farsiu et al. These algorithms are of practical interest because they are fast and also they can overcome mismatches in the assumed acquisition model. However, they rely on the use of steepest-descent minimization which is inefficient in highly nonlinear and ill-conditioned problems like super-resolution. In this paper, we use two storage-efficient quasi-Newton algorithms, the memoryless and the limited-memory BFGS algorithms, to improve the performance of the super-resolution approaches proposed by Farsiu et al.