  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.
