A Survey Study of Image Contrast Enhancement with Various Approach

Low light pictures experience the ill effects of severe noise, low brightness, low contrast, etc. The majority of past picture contrast improvement strategies change the tone curve to address the differentiation of an information picture. Those methods, however, often fail in revealing image details because of the limited information in a single image. Deep learning is presently a dynamic research zone in machine learning and pattern recognition society. The methods made up of Deep learning research have quite recently been influencing the investigation of picture upgrade. Images caught in low-light conditions more often than not experience the ill effects of low difference, which expands the difficulty of consequent PC vision errands in an implausible extent. The network takes low light images as input and it can re-enhance the contrast and brightness of the low light image at the same time reduce noise and color distortion. It should be noted that during the training process, any paired images with different exposure time are often used for training and there is no need to carefully select the supervised images which will save a lot.