Single-frame image-based high-dynamic range image generation method

Abstract

The invention discloses a single-frame image-based high-dynamic range image generation method. According to the method, an edge image block set is found through edge detection, and the total distance of the edge image block set is calculated under a camera response inverse function; a corresponding conditional probability model is constructed; a sampling Gaussian mixture model is constructed according to 201 camera response curves in a DoRF database so as to describe a priori probability model; a maximum posteriori probability model is obtained by using a Bayesian framework; new camera response inverse functions are obtained under a condition that the maximum posteriori probability model is maximum; a Levenberg-Marquardt method is adopted to iteratively solve the new camera response inverse functions to obtain an optimal camera response inverse function; and reconstruction is performed according to the optimal camera response inverse function, so that a high-dynamic range image can be obtained. Image dynamic range expansion is performed in a high-brightness area and a low-brightness area simultaneously, and therefore, the problem of artifacts and details loss at the dark area of the image caused by a method only performing image dynamic range expansion in the high-brightness area can be avoided.

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 single frame image. Background technique [0002] In recent years, high dynamic range imaging technology has not only attracted the attention of academia, but also received extensive attention from the industry. Researchers have proposed many methods for obtaining high dynamic range images. [0003] One of the most common methods is to use multiple low dynamic range images captured at different exposures of the same scene to generate a high quality high dynamic range image. However, most of the images actually captured by people often only have a single exposure, and there are also a large number of single-exposure images in reality. Therefore, researchers began to focus on the method of generating high dynamic range images from single frame low dynamic range images, and made some progress. ...

AI Technical Summary

Problems solved by technology

Most of the existing algorithms use the local expansion model. Although the local expansion takes into account the local characteristics of the image, the local expansion will produce contour effects on the connecting edges of different regions

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