Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77633
Title: Learning a deep single image contrast enhancer from multi-exposure images
Authors: Cai, J 
Gu, S 
Zhang, L 
Keywords: Convolutional neural network
Multi-exposure image fusion
Single image contrast enhancement
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2018, v. 27, no. 4, 8259342, p. 2049-2062 How to cite?
Journal: IEEE transactions on image processing 
Abstract: 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 paper, we propose to use the convolutional neural network (CNN) to train a SICE enhancer. One key issue is how to construct a training data set of low-contrast and high-contrast image pairs for end-to-end CNN learning. To this end, we build a large-scale multi-exposure image data set, which contains 589 elaborately selected high-resolution multi-exposure sequences with 4,413 images. Thirteen representative multi-exposure image fusion and stack-based high dynamic range imaging algorithms are employed to generate the contrast enhanced images for each sequence, and subjective experiments are conducted to screen the best quality one as the reference image of each scene. With the constructed data set, a CNN can be easily trained as the SICE enhancer to improve the contrast of an under-/over-exposure image. Experimental results demonstrate the advantages of our method over existing SICE methods with a significant margin.
URI: http://hdl.handle.net/10397/77633
ISSN: 1057-7149
EISSN: 1941-0042
DOI: 10.1109/TIP.2018.2794218
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