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

A CS-CHMT, multi-focus image technology, applied in the field of multi-focus noise image fusion, can solve the problems of low fusion quality, easy to be affected by noise, etc., to achieve the effect of high fusion quality, not easy to be affected by noise, and reduce running time

Inactive Publication Date: 2014-08-27
无锡金帆钻凿设备股份有限公司
View PDF1 Cites 14 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Multi-focus noise image fusion method based on CS-CHMT and IDPCNN
  • Multi-focus noise image fusion method based on CS-CHMT and IDPCNN
  • Multi-focus noise image fusion method based on CS-CHMT and IDPCNN

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0030]An embodiment of the present invention will be described in detail below with reference to the accompanying drawings. This embodiment is carried out on the premise of the technical solution of the present invention, and provides detailed implementation methods and specific operation processes.

[0031] like figure 1 As shown, this embodiment includes the following specific steps:

[0032] (1) For multi-focus noise image NI A , NI B Perform circular translation operation, then perform Contourlet transformation on the translated image, and decompose to obtain subband coefficients of different scales and directions (j=1, 2..., J; k=1, 2,..., m j ), j represents the decomposition scale, and k represents the number of directional subbands decomposed at each scale, where the scale decomposition LP selects “9-7” biorthogonal filter, the direction filter bank DFB selects “pkva”, and the direction decomposition parameter is set to [ 2, 2, 3, 3], that is, to perform 4-scale d...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/50
Inventor 罗强罗晓清关彪张红英吴小俊张战成
Owner 无锡金帆钻凿设备股份有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products