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The illumination of improperly exposed photographs has been widely correctedusing deep convolutional neural networks or Transformers. Despite withpromising performance, these methods usually suffer from large parameteramounts and heavy computational FLOPs on high-resolution photographs. In thispaper, we propose extremely light-weight (with only ~8K parameters) Multi-ScaleLinear Transformation (MSLT) networks under the multi-layer perceptionarchitecture, which can process 4K-resolution sRGB images at 125Frame-Per-Second (FPS) by a Titan RTX GPU. Specifically, the proposed MSLTnetworks first decompose an input image into high and low frequency layers byLaplacian pyramid techniques, and then sequentially correct different layers bypixel-adaptive linear transformation, which is implemented by efficientbilateral grid learning or 1x1 convolutions. Experiments on two benchmarkdatasets demonstrate the efficiency of our MSLTs against the state-of-the-artson photo exposure correction. Extensive ablation studies validate theeffectiveness of our contributions. The code is available athttps://github.com/Zhou-Yijie/MSLTNet.

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