Deep Learning Based Method Of Low-illumination Image Enhancement

With the rapid development of computer vision,multimedia data such as images and videos play a more and more important role in data transmission which is widely used in public safety,industrial production,medical diagnosis and other fields.Due to the limitations of lightness and camera hardware,images captured at low illumination environment often suffer from low brightness,low contrast and the loss of details.These problems seriously affect subsequent image analysis,so a effective enhancement algorithm is needed to improve the quality of low illumination images.So far,most of the existing algorithms tend to rely on the prior information and constraints of artificial design which cannot accurately capture the deep structure characteristics of images and have shortcomings in robustness,real-time,etc.With the help of massive training data and powerful GPU,we present a low illumination image enhancement method based on deep learning.The main research works and experiment results are as follows:1.We proposed a low illumination image enhancement method based on convolution neural network(CNN).Firstly,we researched the principle of CNN for image enhancement and the key technology of improve the performance of CNN.Then,we construct our CNN model based on a encoder-decoder architecture which aims to restore the content and structure information of low illumination image by aggregating the underlying detail features and advanced semantic features.considering the problem that the traditional pixel-level loss function tend to blur the details,the perceptual loss is introduced to measure advanced semantic differences,so,the final loss function is the fusion of the two kinds of loss.2.Based on the proposed CNN model,we further proposed a low-illumination image enhancement method based on generation adversarial network(GAN).This method uses adversarial network as basic structure,and introduces the adversarial loss,contentaware loss and color loss.Color loss smooths the image texture by Gaussian blur,which effectively correcting the color distortion between the generated image and the real image,beyond that,improving the stability of training process as well as the robustness of algorithm.The proposed low-illumination image enhancement method is compared with other existing enhancement methods with the test data selected at different scenes.We analysis the comparison result with both visual and objective measurement.Experiments show that the proposed method achieves best result in both visual and objective measurements,effectively enhances low-light images,improves image contrast,brightness and lost image details.