Very Lightweight Photo Retouching Network with Conditional Sequential Modulation

TL;DR

This paper introduces CSRNet, a highly efficient photo retouching network that leverages the mathematical properties of global pixel operations, achieving state-of-the-art results with significantly fewer parameters.

Contribution

The paper proposes a novel lightweight framework for global photo retouching based on MLP formulation, with potential extension to local enhancements, outperforming existing methods in efficiency and accuracy.

Findings

Achieves state-of-the-art performance on MIT-Adobe FiveK dataset.

Contains less than 37K trainable parameters, much smaller than existing methods.

Can be extended to local enhancement tasks with competitive results.

Abstract

Photo retouching aims at improving the aesthetic visual quality of images that suffer from photographic defects, especially for poor contrast, over/under exposure, and inharmonious saturation. In practice, photo retouching can be accomplished by a series of image processing operations. As most commonly-used retouching operations are pixel-independent, i.e., the manipulation on one pixel is uncorrelated with its neighboring pixels, we can take advantage of this property and design a specialized algorithm for efficient global photo retouching. We analyze these global operations and find that they can be mathematically formulated by a Multi-Layer Perceptron (MLP). Based on this observation, we propose an extremely lightweight framework -- Conditional Sequential Retouching Network (CSRNet). Benefiting from the utilization of $1\times1$ convolution, CSRNet only contains less than 37K trainable parameters, which are orders of magnitude smaller than existing learning-based methods. Experiments show that our method achieves state-of-the-art performance on the benchmark MIT-Adobe FiveK dataset quantitively and qualitatively. In addition to achieve global photo retouching, the proposed framework can be easily extended to learn local enhancement effects. The extended model, namely CSRNet-L, also achieves competitive results in various local enhancement tasks. Codes are available at https://github.com/lyh-18/CSRNet.