Image fusion method of compressed sensing framework

Abstract

The invention relates to the digital image processing field and the image fusion algorithm field, for greatly improving the running speed of the whole fusion system on the premise that the quality and detail of the image are not impacted, the data volume of the image is greatly reduced and the amount of the data to be processed for fusion process is promoted. Therefore, according to the technical scheme, the image fusion method of compressed sensing framework comprises following steps: step 1, executing the single layer wavelet decomposition on the image to be fused; step 2, selecting the Gaussian along matrix as the measure matrix, measuring and obtaining the value; step 3, selecting the numerical value corresponding to each point for forming the new matrix; step 4, adopting the principal component analysis method for fusing the high-frequency components of the source images after measured; step 4, obtaining the high-frequency component, which is suitable for the wavelet inverse transform; and step 5, obtaining the fused image. The image fusion method of compressed sensing framework is mainly used for the digital image processing field.

Description

technical field

[0001] The invention relates to the field of digital image processing and the field of image fusion algorithms. Specifically, it involves image fusion methods under the framework of compressed sensing. Background technique

[0002] Image fusion refers to the process of image processing and computer technology to extract the beneficial information in each channel from the image data collected by multi-source channels about the same target, and finally integrate them into high-quality images to improve image information. utilization rate. Multi-sensor image fusion uses images of the same target obtained by different sensors. After denoising, space-time registration and resampling, an image is obtained by using image fusion technology. Image fusion can overcome the limitations and differences in geometric, spectral and spatial resolution of single sensor images, and improve image quality. However, the current existing technology has the disadvantages of slow ...

Examples

Embodiment Construction

[0026] After the image is transformed by wavelet, the low-frequency sub-band of the image plays an important role in the reconstruction of the image as an approximation component. Therefore, the low-frequency sub-band coefficients of the wavelet decomposition are not measured here, but the high-frequency sub-band coefficients of the wavelet transform of the original image are measured. This selective measurement method only performs a layer of wavelet transform, and a good recovery is obtained. Effect. In this way, on the one hand, the data volume of the restored image can be effectively reduced, and the memory burden can be reduced; on the other hand, the quality of the reconstructed image can be effectively improved, and the image resolution can be increased.

[0027] The specific steps of image fusion under the compressed sensing framework are as follows:

[0028] The first step: perform single-layer wavelet decomposition on the image to be fused, and obtain low-frequency ...

technical field

[0001] The invention relates to the field of digital image processing and the field of image fusion algorithms. Specifically, it involves image fusion methods under the framework of compressed sensing. Background technique

[0002] Image fusion refers to the process of image processing and computer technology to extract the beneficial information in each channel from the image data collected by multi-source channels about the same target, and finally integrate them into high-quality images to improve image information. utilization rate. Multi-sensor image fusion uses images of the same target obtained by different sensors. After denoising, space-time registration and resampling, an image is obtained by using image fusion technology. Image fusion can overcome the limitations and differences in geometric, spectral and spatial resolution of single sensor images, and improve image quality. However, the current existing technology has the disadvantages of slow ...

Examples

Embodiment Construction

[0026] After the image is transformed by wavelet, the low-frequency sub-band of the image plays an important role in the reconstruction of the image as an approximation component. Therefore, the low-frequency sub-band coefficients of the wavelet decomposition are not measured here, but the high-frequency sub-band coefficients of the wavelet transform of the original image are measured. This selective measurement method only performs a layer of wavelet transform, and a good recovery is obtained. Effect. In this way, on the one hand, the data volume of the restored image can be effectively reduced, and the memory burden can be reduced; on the other hand, the quality of the reconstructed image can be effectively improved, and the image resolution can be increased.

[0027] The specific steps of image fusion under the compressed sensing framework are as follows:

[0028] The first step: perform single-layer wavelet decomposition on the image to be fused, and obtain low-frequency ...

Images