  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.
