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The photographs captured by digital cameras usually suffer from over or underexposure problems. For image exposure enhancement, the tasks of Single-ExposureCorrection (SEC) and Multi-Exposure Fusion (MEF) are widely studied in theimage processing community. However, current SEC or MEF methods are developedunder different motivations and thus ignore the internal correlation betweenSEC and MEF, making it difficult to process arbitrary-length sequences withimproper exposures. Besides, the MEF methods usually fail at estimating theexposure of a sequence containing only under-exposed or over-exposed images. Toalleviate these problems, in this paper, we develop a novel Fusion-CorrectionNetwork (FCNet) to tackle an arbitrary-length (including one) image sequencewith improper exposures. This is achieved by fusing and correcting an imagesequence by Laplacian Pyramid (LP) image decomposition. In each LP level, thelow-frequency base component of the input image sequence is fed into a Fusionblock and a Correction block sequentially for consecutive exposure estimation,implemented by alternative exposure fusion and correction. Theexposure-corrected image in current LP level is upsampled and fused with thehigh-frequency detail components of the input image sequence in the next LPlevel, to output the base component for the Fusion and Correction blocks innext LP level. Experiments on the benchmark dataset demonstrate that our FCNetis effective on arbitrary-length exposure estimation, including both SEC andMEF. The code is publicly released at https://github.com/NKUJinLiang/FCNet.

FCNet: A Convolutional Neural Network for Arbitrary-Length Exposure Estimation