Very Lightweight Photo Retouching Network with Conditional Sequential Modulation

• URL: http://arxiv.org/abs/2104.06279v1
• Date: Tue, 13 Apr 2021 15:11:02 GMT
• Title: Very Lightweight Photo Retouching Network with Conditional Sequential Modulation
• Authors: Yihao Liu, Jingwen He, Xiangyu Chen, Zhengwen Zhang, Hengyuan Zhao, Chao Dong, Yu Qiao
• Abstract summary: We propose an extremely lightweight framework -- Conditional Sequential Retouching Network (CSRNet) 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.
• Score: 42.311196534333284
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Photo retouching aims at improving the aesthetic visual quality of images that suffer from photographic defects such as 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, namly CSRNet-L, also achieves competitive results in various local enhancement tasks. Codes will be available.

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