Multi-focus image fusion method based on compressive sensing

Abstract

The invention discloses a multi-focus image fusion method based on compressive sensing and relates to the technical field of image processing. By the multi-focus image fusion method, the main problem that a clear image with all focused sceneries is difficult to acquire due to a limited depth of field of an optical lens in the prior art can be solved. The multi-focus image fusion method is implemented by the following steps of: (1) blocking an image; (2) calculating an average gradient of each image sub block to determine a fusion weight value; (3) performing sparse representation on each image sub block and observing each image sub block by adopting a random Gaussian matrix; (4) performing weighted fusion on the fusion weight value of an observed value of each image sub block; and (5) recovering a fused image observed value by adopting an orthogonal matching traceback algorithm and performing wavelet inverse transformation on a recovered result to acquire a fused fully-focused image. By the multi-focus image fusion method based on the compressive sensing, a better image fusion effect can be achieved and higher convergence property is realized; and the method can be applied to fusion of a multi-focus image.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to image fusion technology, in particular to a multi-focus image fusion method combined with compressed sensing theory, which can be used in multi-focus image fusion. Background technique [0002] As an emerging scientific research field, image fusion has broad prospects for development. It obtains a more accurate, comprehensive and reliable image description of the same scene or target by extracting and integrating information from multiple sensor images, so as to further analyze and understand the image and detect, identify or track the target. From the early 1980s to the present, multi-sensor image fusion has aroused widespread interest and research boom worldwide, and it has great potential in the fields of automatic target recognition, computer vision, remote sensing, machine learning, medical image processing, and military applications. Wide application prospects. Afte...

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Problems solved by technology

However, there are few studies on applying compressive sensing theory to image fusion.

Scholar T.Wan and others took the lead in applying the theory of compressed sensing to image fusion, see the article "Compressive Image Fusion", in Proc.IEEE Int.Conf.Image Process, pp.1308-1311, 2008. This method uses absolute A fusion rule with a large value not only has high computational complexity, but also has a lot of noise and streaks in the fusion result

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