A Deep Single Image Contrast Enhancer from Multi Exposure Images

Abstract

The natural scene with good contrast, vivid color and rich details is an essential goal of digital photography. The acquired images, however, are often under-exposed or over-exposed because of poor lighting conditions and the limited dynamic range of imaging device. The resulting low contrast and low quality images will not only degenerate the performance of many computer vision and image analysis algorithms, but also degrade the visual aesthetics of images. Contrast enhancement is thus an important step to improve the quality of recorded images and make the image details more visible. Due to the poor lighting condition and limited dynamic range of digital imaging devices, the recorded images are often under-/over-exposed and with low contrast. Most of previous single image contrast enhancement (SICE) methods adjust the tone curve to correct the contrast of an input image. Those methods, however, often fail in revealing image details because of the limited information in a single image. On the other hand, the SICE task can be better accomplished if we can learn extra information from appropriately collected training data. In this work, we propose to use the convolutional neural network (CNN) to train a SICE enhancer. One key issue is how to construct a training dataset of low-contrast and high contrast image pairs for end-to-end CNN learning.