  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.
