Color self-adaptive compression calculation ghost imaging system and method

Abstract

The invention discloses a color self-adaptive compression calculation ghost imaging system and method. The system comprises a PC, a DLP, a color target image, a single-pixel photon detector and a data acquisition and control module. The PC, the DLP and the single-pixel photon detector are connected with the data acquisition and control module. The data acquisition and control module controls the DLP to perform projection at the position of the important coefficients of the Y, U and V components of the color target image under white light, blue light and red light and transmits digital signals obtained through projection of white light, blue light and red light to the PC, and then obtains the respective wavelet important coefficients of the Y, U and V components through linear transformation. Finally inverse wavelet transformation and inverse linear transformation are performed on the Y, U and V components so that the color image is reconstructed. The number of times of sampling is effectively reduced and no loss of important information is guaranteed; and a single-photon camera structure formed by the single single-pixel photon detector and the DLP is utilized so that the system size is reduced and the system structure is simplified.

Description

technical field [0001] The invention belongs to color adaptive computing ghost imaging technology, in particular to a color adaptive compression computing ghost imaging system and method. Background technique [0002] In recent years, scholars have focused more and more attention on the reconstruction of grayscale images using computational ghost imaging technology, and there are relatively few studies on color images. Color images are superior to monochrome and black-and-white images in recognition and detection, so color ghost imaging is the main direction of future development. For the reconstruction of color images, a relatively direct and obvious method is to obtain red, green, and blue three-component images separately, and then fuse the component images to obtain a color image. However, the quality of the color image reconstructed by this method is not good, and the details are not clear enough. Welsh et al. ([1] S S. Welsh, M P. Edgar, R. Bowman, P. Jonathan, B. Su...

AI Technical Summary

Problems solved by technology

Both of the above two color image reconstruction methods use the CS algorithm to reconstruct, but CS also has many disadvantages, for example, when reconstructing an image with a large resolution, the computational overhead is huge and the reconstruction time is too long

Images