Face super-resolution processing method based on K neighbor sparse coding average value constraint

Abstract

The invention discloses a face super-resolution processing method based on K neighbor sparse coding average value constraint, relating to the technical field of image resolution processing, in particular to a face super-resolution processing method based on K neighbor sparse coding average value constraint. The method comprises the following steps of: according to prior information of the position of a face block, clustering image blocks of a training sample to obtain a pair of high-and-low-resolution sparse representation dictionaries in relevant positions; performing sparse representation on K neighbor of the input image block with the low-resolution dictionary, thus obtaining sparse coding average values; and realizing the sparse representation of a low-resolution image block based on sparse prior and K neighbor sparse coding average value constraint, realizing the reconstruction of a high-resolution image block through coefficient mapping, and finally overlapping and averaging to obtain a high-resolution face image. According to the method, on the basis of keeping the similarity of the reconstructed face image, the definition of the face image is improved, and the quality of the super-resolution image is enhanced.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a face super-resolution processing method based on K-nearest neighbor sparse coding mean constraint. Background technique [0002] Face images acquired under video surveillance conditions often have low resolution and poor image quality, so that it is difficult to meet the needs of face recognition. Face super-resolution technology, also known as Face Hallucination, can reconstruct a high-resolution image from one or more low-resolution input face images without changing the hardware environment. face image to achieve the purpose of improving the clarity of the face image. This technology has important applications in security monitoring, computer vision and other fields. [0003] The existing face super-resolution methods can be roughly divided into three categories: the first is the face super-resolution method based on the global parameter model, the sec...

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Problems solved by technology

[0006] Although the existing face super-resolution methods have achieved good results in the ideal super-resolution situation (that is, super-resolution of degraded images with only downsampling), however, when low-resolution images also have noise When , the clarity and similarity of the face image reconstructed by the existing face super-resolution method will be greatly reduced

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