Contrast Enhancement and Image Completion: A CNN Based Model to Restore Ill Exposed Images
Cristiano Rafael Steffens, Lucas Ricardo Vieira Messias, Paulo Drews, Sílvia Silva da Costa Botelho · 2019
Digital cameras work through transforming the scene's radiance into an electrical charge. Optical arrangement, sensors, and embedded electronics often limit the accuracy of the representation. Scenes with a dynamic range above the capability of the camera or poor lighting are challenging conditions, which usually result in low contrast images. Soft clipping is usually compensated by transforming the power and shifting the image's histogram. However, under extreme conditions, ill exposure results in severe clipping that requires interpolation and painting. We introduce a model of convolutional neural network to perform signal reconstruction and interpolation. It is designed to be used on sRGB images. The results are evaluated using several metrics of image quality that indicate that the proposed network can improve images that are damaged by different conditions of exposure. In addition, our method offers a substantial gain over state-of-the-art methods.