Conditional Sequential Modulation for Efficient Global Image Retouching

• URL: http://arxiv.org/abs/2009.10390v1
• Date: Tue, 22 Sep 2020 08:32:04 GMT
• Title: Conditional Sequential Modulation for Efficient Global Image Retouching
• Authors: Jingwen He, Yihao Liu, Yu Qiao, and Chao Dong
• Abstract summary: Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation. In this paper, we investigate some commonly-used retouching operations and mathematically find that these pixel-independent operations can be approximated or formulated by multi-layer perceptrons (MLPs) We propose an extremely light-weight framework - Sequential Retouching Network (CSRNet) - for efficient global image retouching.
• Score: 45.99310982782054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation. Practically, photo retouching can be accomplished by a series of image processing operations. In this paper, we investigate some commonly-used retouching operations and mathematically find that these pixel-independent operations can be approximated or formulated by multi-layer perceptrons (MLPs). Based on this analysis, we propose an extremely light-weight framework - Conditional Sequential Retouching Network (CSRNet) - for efficient global image retouching. CSRNet consists of a base network and a condition network. The base network acts like an MLP that processes each pixel independently and the condition network extracts the global features of the input image to generate a condition vector. To realize retouching operations, we modulate the intermediate features using Global Feature Modulation (GFM), of which the parameters are transformed by condition vector. Benefiting from the utilization of $1\times1$ convolution, CSRNet only contains less than 37k trainable parameters, which is orders of magnitude smaller than existing learning-based methods. Extensive experiments show that our method achieves state-of-the-art performance on the benchmark MIT-Adobe FiveK dataset quantitively and qualitatively. Code is available at https://github.com/hejingwenhejingwen/CSRNet.

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