4K-Resolution Photo Exposure Correction at 125 FPS with ~8K Parameters

The illumination of improperly exposed photographs has been widely corrected using deep convolutional neural networks or Transformers. Despite with promising performance, these methods usually suffer from large parameter amounts and heavy computational FLOPs on high-resolution photographs. In this paper, we propose extremely light-weight (with only ~8K parameters) Multi-Scale Linear Transformation (MSLT) networks under the multi-layer perception architecture, which can process 4K-resolution sRGB images at 125 Frame-Per-Second (FPS) by a Titan RTX GPU. Specifically, the proposed MSLT networks first decompose an input image into high and low frequency layers by Laplacian pyramid techniques, and then sequentially correct different layers by pixel-adaptive linear transformation, which is implemented by efficient bilateral grid learning or 1x1 convolutions. Experiments on two benchmark datasets demonstrate the efficiency of our MSLTs against the state-of-the-arts on photo exposure correction. Extensive ablation studies validate the effectiveness of our contributions. The code is available at https://github.com/Zhou-Yijie/MSLTNet.

Fusion-Correction Network for Single-Exposure Correction and Multi-Exposure Fusion

The photographs captured by digital cameras usually suffer from over-exposure or under-exposure problems. The Single-Exposure Correction (SEC) and Multi-Exposure Fusion (MEF) are two widely studied image processing tasks for image exposure enhancement. However, current SEC and MEF methods ignore the internal correlation between SEC and MEF, and are proposed under distinct frameworks. What's more, most MEF methods usually fail at processing a sequence containing only under-exposed or over-exposed images. To alleviate these problems, in this paper, we develop an integrated framework to simultaneously tackle the SEC and MEF tasks. Built upon the Laplacian Pyramid (LP) decomposition, we propose a novel Fusion-Correction Network (FCNet) to fuse and correct an image sequence sequentially in a multi-level scheme. In each LP level, the image sequence is feed into a Fusion block and a Correction block for consecutive image fusion and exposure correction. The corrected image is upsampled and re-composed with the high-frequency detail components in next-level, producing the base sequence for the next-level blocks. Experiments on the benchmark dataset demonstrate that our FCNet is effective on both the SEC and MEF tasks.