Non-uniform illumination image enhancement method based on virtual multi-exposure fusion

Abstract

The invention discloses a non-uniform illumination image enhancement method based on virtual multi-exposure fusion, and belongs to the field of image processing. The method comprises the following steps: 1) converting an input image from an RGB space to an HSV space; 2) for the V component, determining two optimal exposure ratios based on an image information entropy maximization principle, and performing two times of virtual exposure by using a brightness mapping function to generate an enhanced exposure image V1 and a weakened exposure image V2; 3) fusing an exposure image sequence consisting of V, V1 and V2 by adopting a multi-exposure image fusion method with detail lifting to obtain an enhanced V component; and 4) re-synthesizing an HSV image by using the enhanced V component and the unchanged H component and S component, and converting the HSV image back to the RGB space to serve as a final result to be output. According to the invention, the image quality problem caused by overexposure and underexposure areas existing in the non-uniform illumination image at the same time can be effectively improved, and the image with a high dynamic range and capable of well maintaining color and detail information is generated.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a non-uniform illumination image enhancement method based on virtual multi-exposure fusion. Background technique [0002] In the process of image acquisition, the lighting environment or the reflection of the surface of the object will cause the overall illumination of the image to be uneven, and the existence of overexposure, underexposure or both will cause quality problems such as low contrast and poor visual effect of the image. Enhancing such unevenly illuminated images and improving their visual quality is usually an important preprocessing step for computer vision tasks (such as object detection, object recognition, visual tracking, visual navigation, etc.). At present, the mainstream non-uniform illumination image enhancement methods mainly include the grayscale transformation method represented by Histogram Equalization (HE) and the image enhancement method based on Retin...

AI Technical Summary

Problems solved by technology

This type of method is suitable for processing images with low local gray values, and can effectively enhance the contrast and details of dark areas, but may cause blurred edges of the image while enhancing the image

Since images with uneven illumination generally have both overexposed areas and underexposed areas, the above single image enhancement method based on histogram equalization or Retinex theory is difficult to simultaneously enhance the overexposed areas and underexposed areas in the image. overexposed areas, resulting in distorted colors and loss of detail in overly dark or overly bright areas of the enhanced image

Ying et al. proposed a low-light image enhancement framework based on camera response model and exposure fusion (reference Ying Z, Li G, RenY, et a1: A New Low-Light Image Enhancement Algorithm Using Camera Response Model, ICCV 2017; Ying Z, Li G , RenY, et al: A New Image Contrast Enhancement Algorithm Using Exposure FusionFramework, ICCAIP 2017), the proposed exposure fusion method is only suitable for the fusion of two images. The details of the image will be distorted, so this method is not effective in enhancing unevenly illuminated images that generally have both overexposed and underexposed areas

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