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

The photographs captured by digital cameras usually suffer from the improper (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 inaccurate 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 an integrated convolutional neural network feasible to tackle an arbitrary-length (including one) image sequence suffering from inaccurate exposures. Specifically, we propose a novel Fusion-Correction Network (FCNet) to fuse and correct an image sequence by employing the multi-level Laplacian Pyramid (LP) image decomposition scheme. In each LP level, the low-frequency base component(s) of the input image sequence is fed into a Fusion block and a Correction block sequentially for consecutive exposure estimation, implemented by alternative image fusion and exposure correction. The exposure-corrected image in current LP level is upsampled and re-composed with the high-frequency detail component(s) of the input image sequence in the next LP level, to output the base component of the input image sequence for the Fusion and Correction blocks in the next LP level. Experiments on the benchmark dataset demonstrate that our FCNet is effective arbitrary-length exposure estimation (both SEC and MEF). The code will be publicly released.