Local restriction iteration neighborhood embedding-based face hallucination method

Abstract

The invention relates to a local restriction iteration neighborhood embedding-based face hallucination method. The method comprises the following steps of: establishing high-resolution and low-resolution image block sets to be used as high-resolution and low-resolution image block dictionaries; sampling on inputted image blocks of a low-resolution face image to obtain estimation high-resolution image blocks, seeking K nearest image blocks at the corresponding position in the high-resolution image block dictionary, and expressing the inputted low-resolution image blocks by using the corresponding K low-resolution image blocks to acquire a weight coefficient; reconstructing K neighbor high-resolution image blocks by utilizing the weight coefficient to form new estimation high-resolution image blocks, and performing the operation repeatedly until the most satisfied estimation high-resolution image blocks are obtained; and integrating into a high-resolution image according to the positional relations of the low-resolution image blocks. According to the method, two manifold structures are considered simultaneously on the basis of position apriority and local manifold restriction, and K neighbor points and reconstruction weights are updated continuously in an iteration form on the basis of a result of last reconstruction to achieve a high-quality reconstruction effect which is close to the real condition.

Description

technical field [0001] The invention relates to the field of image super-resolution, in particular to a face illusion method based on local constraint iterative neighborhood embedding. Background technique [0002] In the past 20 years, face recognition technology has developed rapidly. At the same time, due to the limited network bandwidth and server storage limitations of the video surveillance system, the resolution of the captured facial images is low, so that the facial information that can be provided is very limited, which has become one of the most challenging problems in biometrics. Recently, super-resolution techniques have been used to process low-resolution (LR) images, which can generate a sequence of low-resolution images or a single frame of low-resolution images that can provide more faces for the subsequent recognition process. High-Resolution (HR) images of details. [0003] In 2000, Baker and Kanade proposed a kind of face hallucination in literature 1 (...

AI Technical Summary

Problems solved by technology

All the methods mentioned above only consider the manifold of low-resolution image blocks, while ignoring the geometric structure information of high-resolution image blocks, making the reconstruction results lack of reliability and discrimination

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