Abstract:

Proposed in this paper is a novel super-resolution reconstruction algorithm of single-frame images,which integrates the improved super-resolution reconstruction based on manifold learning with the regularized reconstruction based on gradient constraint. In this algorithm,a new feature extraction method,which combines the two feature vectors of the normalized luminance and the detail sub-band coefficient of stationary wavelet transform,is put forward for the super-resolution reconstruction based on manifold learning,and is used to improve the reconstruction performance. Then,a regularized reconstruction based on gradient constraint is implemented to obtain the final high-resolution image,with the learned high-resolution image and its gradient respectively as the initial estimate and the target gradient field. As compared with some existing algorithms,the proposed algorithm is of better reconstruction performance in terms of both visual effect and objective evaluation.

Key words: image processing, super-resolution reconstruction, manifold learning, gradient constraint, regularized reconstruction