Using Convolutional Neural Network for Image Enhancement on Mobile Devices This paper presents an application used to automatically enhance an image captured by a camera of a mobile device. The proposed solution consists of applying 4 image enhancement algorithms: gamma correction, saturation correction, white and black level correction. In order to parameterize the algorithms, we proposed a solution based on a convolutional neural network. The network was ported on a mobile device, keeping in mind to minimize computation resources and battery consumption. The mobile platform used is a smart phone, which has an accelerator, useful for massive processing of data caused by the neural network and a graphical processor, used to apply the 4 algorithms.

Contrast Enhancement and Image Completion: A CNN Based Model to Restore Ill Exposed Images Digital cameras work through transforming the scenexe2x80x99s 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 imagexe2x80x99s 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 stateoftheart methods.