  Photo retouching aims to adjust the luminance, contrast, and saturation of
the image to make it more human aesthetically desirable. However, artists'
actions in photo retouching are difficult to quantitatively analyze. By
investigating their retouching behaviors, we propose a two-stage network that
brightens images first and then enriches them in the chrominance plane. Six
pieces of useful information from image EXIF are picked as the network's
condition input. Additionally, hue palette loss is added to make the image more
vibrant. Based on the above three aspects, Luminance-Chrominance Cascading
Net(LCCNet) makes the machine learning problem of mimicking artists in photo
retouching more reasonable. Experiments show that our method is effective on
the benchmark MIT-Adobe FiveK dataset, and achieves state-of-the-art
performance for both quantitative and qualitative evaluation.
