RGB color constancy using multispectral pixel information

Abstract

Multispectral imaging is a technique that captures data across several bands of the light spectrum, and it can be useful in many computer vision fields, including color constancy. We propose a method that exploits multispectral imaging for illuminant estimation, and then applies illuminant correction in the raw RGB domain to achieve computational color constancy. Our proposed method is composed of two steps: first, a selected number of existing camera-independent algorithms for illuminant estimation, originally designed for RGB data, are applied in generalized form to work with multispectral data. We demonstrate that the sole multispectral extension of such algorithms is not sufficient to achieve color constancy, and thus we introduce a second step, in which we re-elaborate the multispectral estimations before conversion into raw RGB with the use of the camera response function. Our results on the NUS dataset show that an improvement of 60% in the color constancy performance, measured in terms of reproduction angular error, can be obtained according to our method when compared to the traditional raw RGB pipeline.

Dual-Channel Mapping–Gas Column Concentration Inversion Method Based on Multispectral Imaging

“…Compared with the active method, passive infrared gas imaging detection technology has the advantages of simple structure, more types of detectable gases, no active light source, etc. However, it requires a temperature difference between the measured gas and the background and a low signal-to-noise ratio, making it suitable for gas detection under harsh environments and background temperature differences [15][16][17][18][19][20]. Currently, passive infrared gas imaging detection technology can be divided into hyperspectral imaging technology and multispectral imaging technology according to the number of detection bands.…”