Publication

Abstract : The use of deep learning to hone image processing techniques has become increasingly popular. Following the success of Convolutional Neural Networks (CNNs) for image classification, they have been tested for various applications. By training CNNs on a dataset with ground truth (light) images and the corresponding darkened version of the images, neural networks can be used for enhancement. This must account for the non-uniform illumination seen in night-time images. A novel method of training a neural network to enhance non-uniformly illuminated images is proposed. Further, the visualization of convolutional features extracted at each layer of the neural network is discussed, to understand which parts of an image helps the neural network identify the object, thereby enhancing its recognition power. The potential application of this system lies in detecting animals in the non-uniformly lit surveillance video, useful to settlements near forest regions, where wild animals pose a threat to the living areas.