A High Dynamic Range Image Generation Method Based on Human Vision Model

Abstract

The invention discloses a high dynamic range image generation method based on the human eye visual model. First, the low dynamic range image is decomposed into a brightness image and a chrominance image, and the brightness image is bilaterally filtered to obtain a local brightness image of a pixel, and the calculated Then, according to the local human visual perception model, the brightness image of the low dynamic range image and the local brightness image of the high dynamic range are processed to obtain the brightness image of the high dynamic range image; finally, the The high dynamic range luminance image and the chrominance image of the low dynamic range image are merged to produce a high dynamic range image. The invention obtains a high-quality high dynamic range image through the inverse process of human visual system imaging, that is, the local human visual perception model constructed, and at the same time uses the local brightness information around each pixel to locally process the pixel brightness expansion, thereby solving the problem of Existing methods introduce the problem of detail artifacts or contouring effects.

Description

technical field [0001] The invention belongs to the technical field of image processing, and more specifically relates to a method for generating a high dynamic range image based on a human visual model. Background technique [0002] Since high dynamic range images can better represent the true color range of actual scenes, they have received great attention in image processing related fields in recent years. The corresponding high dynamic range display device is also favored by researchers because it can provide a wider dynamic range and better picture quality. High dynamic range display devices have been used in some research institutions and laboratories. Although they are not popular yet, they are the development trend of the next generation of display devices. The emergence of high dynamic range display devices and the superior performance of high dynamic range images have led researchers to acquire high dynamic range images from the currently popular single-frame low ...

AI Technical Summary

Problems solved by technology

Local transformation uses different transformation functions for different exposures and texture regions in the image. Although the local characteristics of the image are considered, it brings two problems: 1. Due to the use of different transformation functions, the edges of the local region and its surroundings It looks discontinuous, which will lead to the appearance of contour effects; second, this partitioning method will increase the complexity of the entire technology implementation, and may bring annoying artifacts, which will make the image quality after the dynamic range expansion worse. May be worse than the original image quality

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