Low Light GAN-Based Photo Enhancement

This paper is based on a novel learning-based pipeline that replaces the conventional image processing pipeline in order to enhance performance of current multi camera devices for low light photography. This paper proposes a new suggestive algorithm to suggest a better position to capture image based on region segmentation (object-based segmentation), camera calibration, parameter selection, Frame Regeneration (Merging and alignment of frames) and then using Generative Adversarial Network (GAN) for image enhancement. As Low Light Image enhancement is one of the biggest digital image processing problem due to factors like noise, low exposure and incorrect edge detections of the object in low light so image enhancement is difficult these images, this research work proposes a low light image enhancement model by comparing it with current models. Also, the proposed research work is mainly focused on mobile devices as they are currently used by over 90% users to capture the images. The proposed method assists in increasing the SNR by 7.63 % over the other existing approaches.