An Optimized LIME Scheme for Medical Low Light Level Image Enhancement

Abstract

The role of medical image technology in the medical field is becoming more and more obvious. Doctors can use medical image technology to more accurately understand the patient's condition and make accurate judgments and diagnosis and treatment in order to make a large number of medical blurred images clear and easy to identify. Inspired by the human vision system (HVS), we propose a simple and effective method of low-light image enhancement. In the proposed method, first a sampler is used to get the optimal exposure ratio for the camera response model. Then, a generator is used to synthesize dual-exposure images that are well exposed in the regions where the original image is underexposed. Next, the enhanced image is processed by using a part of low-light image enhancement via the illumination map estimation (LIME) algorithm, and the weight matrix of the two images will be determined when fusing. After that, the combiner is used to get the synthesized image with all pixels well exposed, and finally, a postprocessing part is added to make the output image perform better. In the postprocessing part, the best gray range of the image is adjusted, and the image is denoised and recomposed by using the block machine 3-dimensional (BM3D) model. Experiment results show that the proposed method can enhance low-light images with less visual information distortions when compared with those of several recent effective methods. When it is applied in the field of medical images, it is convenient for medical workers to accurately grasp the details and characteristics of medical images and help medical workers analyze and judge the images more conveniently.