Research On Low-Light Image Enhancement Algotithm Based On Retinex And Its Network Model

Low light image refers to the low illumination image captured in a dark environment.Due to the influence of natural environment,such as weather,natural light and shooting angle,the overall low contrast,low brightness and dark area noise of the low light image as a whole are not in line with the visual requirements of human eyes,and it brings great difficulties for the subsequent image processing,such as visual navigation,target positioning,intelligent monitoring,etc.Therefore,the research of natural low light image to improve the quality of low illumination image and enhance the details of the image has important scientific significance for image processing and application.Aiming at the problem of low light image enhancement,this paper introduces the advantages and disadvantages of common image enhancement algorithms,and deeply studies the Retinex theory and Retinex-Net network model of image enhancement methods.By analyzing the problems of image enhancement in different scenes,the related improved algorithms are proposed to meet the subjective and objective quality requirements of enhanced images.The main work of this paper is as follows:1.A multiple image fusion enhancement algorithm based on Retinex is proposed to solve the problem of contrast enhancement and naturalness preservation under the low light conditions.The algorithm can save the natural degree and detail information of the image effectively while improving the brightness and contrast of the image.The experimental results show that the proposed algorithm has lower LOE and NIQE values,the downsampling low light image can be reduced to 4.12 and 3.25 at the same time,and the single image detection time is 18.4 ms.compared with other single image enhancement methods,it can show better enhancement effect and reduce the time complexity of the processing process.2.An improved Retinex-Net model and a loss function with color restore is proposed to solve the problem of color distortion.Through the two-stage decomposition and synthesis training,the model ensures that the training process of each stage can reach the local optimum,so as to improve the enhancement quality of the model.The experimental results show that the average LOE and NIQE values of the proposed network model for LOw-Light dataset test images can be reduced to 942 and 6.42,which is better than Retinex-Net and other low light enhancement methods in subjective and objective image quality,and can maintain good generalization ability in non-uniform illumination and backlight images.To sum up,this paper analyzes and summarizes Retinex and Its network model systematically.By combining multiple image fusion,weighted least squares filtering and convolution neural network technology,the quality of low light image enhancement is greatly improved,which provides a new solution for the existing image enhancement.