eLIR-Net: an Efficient AI Solution for Image Retouching

Picture quality serves as a primary differentiator for prominent display panel manufacturers. AI-based solutions have made remarkable progress in delivering expert-level image color remastering operations. However, their demand on intensive computation resources heavily impedes the on-device usage in industries where speed and scale are crucial. In this paper, we propose an extremely lightweight image-retouching network (eLIR-Net) that can be deployed on resource-restricted hardware like mobile terminals and embedded devices. The eLIR-Net takes in easily computable and intuitive color distributions to represent the macro view of an image, which is encoded by a well-designed condition network to guide the generative base network to converge in the desired direction, thus generating visually captivating outputs. Both quantitative and qualitative results show the proposed eLIR-Net can achieve equivalent or superior performance compared to the benchmark models at an affordable cost, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{6. 8 K}$</tex> parameters, which is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 4. 1 \%}$</tex> the size of the smallest state-of-the-art network to the best of our knowledge. This work showcases the possibility that compact models deliver competitive performance compared to large models with affordable cost that enables the benefits of AI to be shared across a wider range of industrial applications.