NamedCurves: Learned Image Enhancement via Color Naming

TL;DR

NamedCurves is a learning-based image enhancement method that manipulates images through a small set of named colors, combining tone curves and attention mechanisms to improve editing quality on standard datasets.

Contribution

It introduces a novel approach that separates images into named colors and learns to adjust each via tone curves, enabling intuitive and effective image enhancement.

Findings

Outperforms competing methods on Adobe 5K dataset

Shows notable improvements on PPR10K dataset

Effective in mimicking spatial editing through attention-based fusion

Abstract

A popular method for enhancing images involves learning the style of a professional photo editor using pairs of training images comprised of the original input with the editor-enhanced version. When manipulating images, many editing tools offer a feature that allows the user to manipulate a limited selection of familiar colors. Editing by color name allows easy adjustment of elements like the "blue" of the sky or the "green" of trees. Inspired by this approach to color manipulation, we propose NamedCurves, a learning-based image enhancement technique that separates the image into a small set of named colors. Our method learns to globally adjust the image for each specific named color via tone curves and then combines the images using an attention-based fusion mechanism to mimic spatial editing. We demonstrate the effectiveness of our method against several competing methods on the well-known Adobe 5K dataset and the PPR10K dataset, showing notable improvements.