Research On Colour Image Enhancement Algorithms In Low Illumination Environments

Low-illumination imaging,with its low brightness,blurred details,low contrast and high noise content,can seriously affect the recognition of image information and subsequent acquisition of useful information by the human eye and machines.Therefore,it is of great importance to study low-light image enhancement algorithms.In this paper,two improved algorithms based on Retinex theory are proposed,with the following details.(1)In response to the problems of colour distortion and halo artefacts in low-illumination image enhancement algorithms,this paper proposes a low-illumination colour image enhancement method based on image fusion.The method processes the I-channel in the HSI colour space,and then uses a linear colour recovery algorithm to convert the image from HSI back to RGB colour space,thus effectively avoiding colour distortion.Secondly,the method uses a weighted bootstrap filter instead of the Gaussian filter in the traditional Retinex algorithm,which allows for better estimation of the illumination component due to the anisotropic nature of the weighted bootstrap filter.The estimated illumination components are then replicated and an adaptive illumination adjustment function is applied to one of the illumination components for luminance enhancement;an S-type hyperbolic tangent function is applied to the other illumination component to widen the dynamic range of the grey scale for contrast enhancement;finally,the weights are solved using the PCA method and the detailed features are extracted from the two images for weighted fusion.The experimental results show that the images enhanced by this method strike a balance between local contrast improvement and maintaining the visual naturalness of the images,and that the method can improve the overall brightness and contrast of the images compared with classical algorithms,while reducing the impact of low-illumination environments on the imaging effect.(2)The existing Retinex-based image enhancement algorithm suffers from blurred edges,unremarkable detail texture and ineffective noise elimination after image enhancement under uneven illumination or too dark conditions.To address the above problems,this paper proposes to improve the Retinex algorithm by using gradient domain guided filtering and multi-scale detail enhancement algorithm.Firstly,the input image is converted to HSI color space,which effectively avoids color distortion.Secondly,gradient domain guided filtering is used to estimate the illumination and remove the noise.Gradient domain bootstrap filter is a kind of filter with first-order edge-aware characteristics,so it can effectively avoid local blur and halo while maintaining edge details.Finally,multi-scale detail enhancement is introduced to enhance the image dark details.Since the multi-scale detail enhancement algorithm performs weighted fusion of three different detail layers of the image,the image dark details are effectively enhanced.The experimental results show that the method is effective in edge preservation,noise elimination and dark detail enhancement.