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

Cascade Luminance and Chrominance for Image Retouching: More Like Artist