RGB Illuminant Compensation using Spectral Super-resolution and Weighted Spectral Color Correction

This paper presents a novel approach for spectral illuminant correction in smartphone imaging systems, aiming to improve color accuracy and enhance image quality. The methods introduced include Spectral Super Resolution and Weighted Spectral Color Correction (W-SCC). These techniques leverage the spectral information of both the image and the illuminant to perform effective color correction. Experimental evaluations were conducted using a dataset of 100 synthetic images, whose acquisition is simulated using the transmittance information of a Huawei P50 smartphone camera sensor and an Ambient light Multispectral Sensor (AMS). The results demonstrate the superiority of the proposed methods compared to traditional trichromatic pipelines, achieving significant reductions in colorimetric errors measured in terms of ΔE94 units. The W-SCC technique, in particular, incorporates per-wavelength weight optimization, further enhancing the accuracy of spectral illuminant correction. The presented approaches have valuable applications in various fields, including color analysis, computer vision, and image processing. Future research directions may involve exploring additional optimization techniques and incorporating advanced machine learning algorithms to further advance spectral illuminant correction in smartphone imaging systems.