Image enhancement using CNN

Abstract

This study explores Image Enhancement using CNN experiments conducted using the LOL dataset has the potential to greatly increase the quality of photographs taken in low light. It has been demonstrated that CNN-based low-light picture enhancement techniques perform better than conventional techniques in terms of both quantitative and qualitative criteria. CNNs are excellent for enhancing images in low light because they can discover intricate correlations between picture data. A CNN is usually trained on a dataset of paired low-light and improved pictures as part of CNN-based low-light image improvement techniques. CNN gains the ability to create improved images that resemble the ground truth images in the dataset by extracting characteristics from photos taken in low light. There are several benefits that CNN-based low-light picture enhancing techniques offer over conventional techniques. First, CNNs perform better than conventional techniques because they can understand intricate correlations between visual data. Secondly, fresh low-light pictures can be improved using CNN-based techniques without requiring paired training data. They become more useful and broadly applicable as a result. Third, improved photographs appear more realistic and natural because CNN-based techniques may retain semantic information in low-light photos.