Image Shadow Removal Based on Residual Neural Network The removal of image shadows has always been a challenging task that requires us to detect shadows and understand the surrounding scenes. The existing method of shadow detection and removal first locates the shadow area by shadow detection,and then uses some reconstruction algorithms to remove the shadows of the umbra and penumbra. However, detecting shadows is already a very rare task. Based on the traditional physical methods can be applied to a high quality image, and a method based on statistical characteristics must manually tag shadow. In this paper,we use a convolutional residual neural network to train the model. Using the residual neural network, we can prevent degradation due to the excessive number of network layers. The trained model can detect the shadow area by inputting the global image and combining the semantics of the picture. In these two aspects, good shadow area detection and positioning can be obtained, and image shadow removal can be realized.

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.