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.

翻译：数字相机拍摄的照片常存在过曝或欠曝问题。在图像处理领域，单次曝光校正（SEC）与多曝光融合（MEF）是图像曝光增强的两大研究方向。然而现有SEC或MEF方法基于不同设计理念，忽略了两者间的内在关联，难以处理包含任意长度不当曝光序列的图像。此外，MEF方法在仅包含欠曝或过曝图像的序列中通常无法准确估计曝光值。为解决上述问题，本文创新性地提出融合校正网络（FCNet），可处理包含任意长度（含单张图像）的不当曝光图像序列。该方法通过拉普拉斯金字塔（LP）图像分解实现序列图像的融合与校正：在每层LP分解中，输入图像序列的低频基分量依次经融合模块与校正模块实现连续曝光估计，通过交替曝光融合与校正完成计算；当前LP层校正后的图像经上采样后与下一层LP输入图像序列的高频细节分量融合，生成下一层融合模块与校正模块所需的基分量。基准数据集实验表明，FCNet在SEC与MEF两种场景下均能有效实现任意长度序列的曝光估计。