Degraded image restoration and identification method based on sparse low-rank prior

Abstract

The invention relates to a degraded image restoration and identification method based on sparse low-rank prior. According to the method, the degraded face image is analyzed, and the degraded face image is recovered and recognized at the same time through sparse low-rank priori of the face image, so that the efficiency of a face recovery algorithm is improved, the recognition accuracy is ensured, and the challenging problem of degraded face recognition is solved. An iterative algorithm is provided, which combines two parts of degraded face image restoration and face recognition. The face image restoration part aligns the face, and the recognition part recognizes the face image. According to the method, sparse low-rank prior is utilized to restore and align the face image, then the restored and aligned face image is input into face recognition, the efficiency of the restoration and alignment algorithm is improved through the recognition effect, and the recognition accuracy is ensured through restoration and alignment. The algorithm solves the problems of image alignment, image de-occlusion, image recognition and the like of the degraded face image, and verifies the effectiveness and advancement of the degraded face image.

Description

technical field [0001] The invention relates to an image restoration and recognition method, in particular to a degraded image restoration and recognition method based on sparse low-rank prior. Background technique [0002] Image is the basis of human vision, an objective reflection of natural scenery, and the most important source for human beings to perceive the objective world and obtain external information. Improving image quality and increasing the application value of images has always been one of the most basic and important research topics in the field of image processing and computer vision, and has attracted extensive attention from scholars in the fields of mathematics, computer vision, and signal processing. This is because images play the most important role in human production and life, and are an important tool for human beings to obtain information and understand the world, especially in today's highly developed science and technology. Painting and calligra...

AI Technical Summary

Problems solved by technology

Usually, in order to solve the problem of degraded image recognition, the method that comes to mind is to first restore the image to obtain a better quality image, and then input the restoration result into the recognition system. The problem with this direct method is that many restoration algorithms are is designed to improve human visual perception, not machine perception, and therefore does not guarantee improved recognition

Worse, when the degradation model is unknown, general restoration algorithms (such as deblurring) perform poorly on some real images (such as human faces) that do not exhibit strong edge structures, and often introduce severe artifacts, thus actually affecting the recognition performance

And separating the execution of the recovery task and the recognition task will lead to a lack of information interaction between each other, that is, the recovery task execution process does not consider the recognition requirements, and the recognition task does not necessarily require the recovery task to provide too high global image clarity

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