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

Image and Video Processing;Computer Vision and Pattern Recognition