Spectral Characterization of a Prototype SFA Camera for Joint Visible and NIR Acquisition

International audienceMultispectral acquisition improves machine vision since it permits capturing more information on object surface properties than color imaging. The concept of spectral filter arrays has been developed recently and allows multispectral single shot acquisition with a compact camera design. Due to filter manufacturing difficulties, there was, up to recently, no system available for a large span of spectrum, i.e., visible and Near Infra-Red acquisition. This article presents the achievement of a prototype of camera that captures seven visible and one near infra-red bands on the same sensor chip. A calibration is proposed to characterize the sensor, and images are captured. Data are provided as supplementary material for further analysis and simulations. This opens a new range of applications in security, robotics, automotive and medical fields

Analysis of image noise in multispectral color acquisition

The design of a system for multispectral image capture will be influenced by the imaging application, such as image archiving, vision research, illuminant modification or improved (trichromatic) color reproduction. A key aspect of the system performance is the effect of noise, or error, when acquiring multiple color image records and processing of the data. This research provides an analysis that allows the prediction of the image-noise characteristics of systems for the capture of multispectral images. The effects of both detector noise and image processing quantization on the color information are considered, as is the correlation between the errors in the component signals. The above multivariate error-propagation analysis is then applied to an actual prototype system. Sources of image noise in both digital camera and image processing are related to colorimetric errors. Recommendations for detector characteristics and image processing for future systems are then discussed

Computational multi-spectral video imaging

Multi-spectral imagers reveal information unperceivable to humans and conventional cameras. Here, we demonstrate a compact single-shot multi-spectral video-imaging camera by placing a micro-structured diffractive filter in close proximity to the image sensor. The diffractive filter converts spectral information to a spatial code on the sensor pixels. Following a calibration step, this code can be inverted via regularization-based linear algebra, to compute the multi-spectral image. We experimentally demonstrated spectral resolution of 9.6nm within the visible band (430nm to 718nm). We further show that the spatial resolution is enhanced by over 30% compared to the case without the diffractive filter. We also demonstrate Vis-IR imaging with the same sensor. Furthermore, our camera is able to computationally trade-off spectral resolution against the field of view in software without any change in hardware as long as sufficient sensor pixels are utilized for information encoding. Since no absorptive color filters are utilized, sensitivity is preserved as well. Finally, the diffractive filters can be easily manufactured using optical lithography and replication techniques

The Effects of Multi-channel Visible Spectrum Imaging on Perceived Spatial Image Quality and Color Reproduction Accuracy

Two paired-comparison psychophysical experiments were performed. The stimuli consisted of six image types resultingfrom several multispectral image-capture and reconstruction techniques. A seventh image type, color-managed images from a high-end consumer camera, was also included in thefirst experiment to compare the accuracy of commercial RGB imaging. The images were evaluated under simulated daylight (6800K) and incandescent (2700K) illumination. The first experiment evaluated color reproduction accuracy. Under simulated daylight, the subjects judged all of the images to have the same color accuracy, except the consumer camera image which was significantly worse. Under incandescent illumination, all the images, including the consumer camera, had equivalent performance. The second experiment evaluated image quality. The results of this experiment were highly target dependent. A subsequent image registration experiment showed that the results of the image quality experiment were affected by image registration to some degree. An analysis of the color reproduction accuracy and image quality experiments combined showed that the consumer camera image type was preferred the least over all. The most preferred image types were the thirty-one-channel image type and both six-channel image types created using RGB filters along with a Wratten filter, with eigenvector analysis and pseudo-inverse transformations