Multi-focus noise image fusion method based on CS-CHMT and IDPCNN

Abstract

The invention discloses a multi-focus noise image fusion method based on a cycle spinning-Contourlet domain hidden Markov tree model (CS-CHMT) and an improved dual-channel pulse-coupled neural network (IDPCNN). First, two multi-focus images containing a certain level of Gauss white noise are de-noised by use of the CS-CHMT model, and on the basis, a fusion strategy is designed by use of the IDPCNN to obtain a final fused image. According to the invention, by making use of the directional sensitivity of Contourlet transform height and the advantages of anisotropy, performing image de-noising by use of a hidden Markov tree (HMT) model, and introducing a cycle spinning technology to effectively suppress the pseudo Gibbs effect of the images near singular points, the PSNR value of de-noised images is improved. Compared with the traditional multi-focus image fusion method, the improved IDPCNN fusion method can effectively preserve more detailed information characterizing image features, greatly improve the quality of fused images and further improve the visual effect, and has real-time performance.

Description

technical field [0001] The invention relates to a multi-focus noise image fusion method based on cyclic translation combined with Contourlet domain hidden Markov tree model (CS-CHMT) and improved dual-channel pulse-coupled neural network (IDPCNN), which is a new technology in the field of digital image processing technology. Item fusion method is widely used in digital camera, machine vision, target recognition and other systems. Background technique [0002] As an important branch of data fusion, image fusion integrates modern high-tech such as sensors, image processing, signal processing, computer and artificial intelligence, and is an indispensable item in the field of image understanding and computer vision. Among them, multi-focus image fusion has always been a research hotspot in the field of image fusion, that is, through a certain algorithm, two or more images that have been registered and have the same imaging conditions but different focus are synthesized into a ne...

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Problems solved by technology

[0006] The purpose of the present invention is to address the deficiencies of the traditional fusion technology in the existing space domain, and propose a method based on cyclic translation combined with Contourlet domain hidden Markov tree model (CS-CHMT) and improved dual-channel pulse-coupled neural network (IDPCNN). The multi-focus noise image fusion method solves the problem that the existing technology is easily affected by noise and the fusion quality is not high in the multi-focus image fusion, and obtains a fusion image with better objective indicators and subjective visual effects

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