ReCC-GAN: scene global illuminant estimation network based on Retinex and generative adversarial network

Abstract

In capturing images, cameras often encounter color-cast problems owing to variations in the illumination of different scenes. Existing algorithms attempt to estimate the illuminant information from the raw image data captured by a camera sensor to address color-casting problems. However, these algorithms tend to overlook the potential interference of scene brightness information present in the raw data. Therefore, we introduce a novel illuminant estimation architecture, Retinex color constancy generative adversarial network (ReCC-GAN). ReCC-GAN aims to mitigate the impact of scene brightness information on illuminant estimation tasks, enhance global illuminant estimation accuracy, and resolve color-cast problems. The ReCC-GAN architecture initially separates the reflectance component from the raw data using the Retinex module, reducing the interference caused by the scene brightness information. Subsequently, a specially designed GAN for illuminant estimation was employed to estimate the illuminant vector from this component more effectively. This approach improved illumination estimation accuracy. Experimental results on the Cube++ and NUS-8 dataset demonstrated that the proposed algorithm achieves competitive results compared with other solutions, with outstanding performance on the worst 25% metric. Additionally, tests on various datasets confirmed that the ReCC-GAN exhibited strong generalization performance, further validating its effectiveness and rationality in scene-wide illuminant estimation tasks.