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Very Lightweight Photo Retouching Network With Conditional Sequential Modulation
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AI
DOI：10.1109/TMM.2022.3179904.png)
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Abstract
En 中文
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 $1\times 1$ convolution, CSRNet only contains less than 37 K 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.
Keywords:
Image color analysis
Modulation
Image enhancement
Task analysis
Visualization
Training
Reinforcement learning
Feature modulation
image enhancement
multi-layer perceptron
neural networks
photo retouching
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