Recovering spectral information using digital camera systems

Abstract

There is an increasing need to be able to measure colour properties of complex surfaces or images for which traditional spectrophotometers are not suitable. New multispectral imaging systems are being developed but it is not clearly understood how the parameters (such as the number of colour channels, the spectral properties of the channels, and the choice of illuminant) of such systems affect the performance. Furthermore, the effect of sensor and quantisation noise on the overall performance of the system also needs to be considered. This paper describes the development of a mathematical model of a multispectral imaging system that takes into account imaging parameters and noise. The results from the computational model show that increasing the number of colour channels alone in the imaging system does not necessarily allow better estimates of spectral reflectance. The choice of illumination can also, in the presence of noise, greatly affect performance.

Illuminant estimation in multispectral imaging

“…Figure 5 shows the poor performance of illuminant estimation for I3 and I5. At this stage, the trend of improvement in max-spectral can already be observed, which becomes clear with eight channels as max-spectral S 50 8 performs best for all images except I6, which works well with spectral gray edge S 50 8 . The performance of max-spectral S 50 8 for images I3 and I5 is shown in Fig.…”

“…Extending the use of the multispectral imaging system from heavily constrained environments to real-world applications is still an open challenge. One of the major obstacles is calibration of the multispectral camera according to the scene illuminant [7][8][9][10][11]. In this work, we investigate the use of illuminant estimation algorithms for multispectral imaging systems.…”

Multispectral synthesis of daylight using a commercial digital CCD camera

“…[1][2][3][4][5] The main application is spectral recovery, and therefore these techniques have been applied intensively in areas as diverse as artificial vision, industrial colorimetry, biomedical engineering, and medicine. Of particular interest is their application in atmospheric optics and remote sensing, where it is difficult to obtain high spectral and high spatial resolution at low cost.…”

Estimation of the Concentration of Dyes in Clear Solutions Using a Digital Camera

“…During the last decade, great attention has been paid to estimate spectral data from colorimetric information, mostly in the RGB form. [1][2][3][4][5] Spectral reconstruction techniques are one of many problems which employ different mathematical solutions, and can generally be classified in various categories. One of the most applicable techniques is called learning-based or indirect reconstruction, which needs a calibration target to achieve a relationship between camera signals and spectral information.…”