Multispectral image acquisition considerably improves color accuracy in comparison to RGB technology. A common multispectral camera design concept features a filter-wheel consisting of six or more optical bandpass filters. By shifting the filters sequentially into the optical path, the electromagnetic spectrum is acquired through the channels, thus making an approximate reconstruction of the spectrum feasible. However, since the optical filters exhibit different thicknesses, refraction indices and may not be aligned in a perfectly coplanar manner, geometric distortions occur in each spectral channel: The reconstructed RGB images thus show rainbow-like color fringes. To compensate for these, we analyze the optical path and derive a mathematical model of the distortions. Based on this model we present two different algorithms for compensation and show that the color fringes vanish completely after application of our algorithms. We also evaluate our compensation algorithms in terms of accuracy and execution time.

Capturing natural scenes with high dynamic range content using conventional RGB cameras generally results in saturated and underexposed and therefore compromising image areas. Furthermore the image lacks color accuracy due to a systematic color error of the RGB color filters. The problem of the limited dynamic range of the camera has been addressed by high dynamic range imaging 1, 2 (HDRI): Several RGB images of different exposures are combined into one image with greater dynamic range. Color accuracy on the other hand can be greatly improved using multispectral cameras, 3 which more accurately sample the electromagnetic spectrum. We present a promising combination of both technologies, a high dynamic range multispectral camera featuring a higher color accuracy, an improved signal to noise ratio and greater dynamic range compared to a similar low dynamic range camera.

Conventional point spread function (PSF) measurement methods often use parametric models for the estimation of the PSF. This limits the shape of the PSF to a specific form provided by the model. However, there are unconventional imaging systems like multispectral cameras with optical bandpass filters, which produce an, e.g., unsymmetric PSF. To estimate such PSFs we have developed a new measurement method utilizing a random noise test target with markers: After acquisition of this target, a synthetic prototype of the test target is geometrically transformed to match the acquired image with respect to its geometric alignment. This allows us to estimate the PSF by direct comparison between prototype and image. The noise target allows us to evaluate all frequencies due to the approximately "white" spectrum of the test target -we are not limited to a specifically shaped PSF. The registration of the prototype pattern gives us the opportunity to take the specific spectrum into account and not just a "white" spectrum, which might be a weak assumption in small image regions. Based on the PSF measurement, we perform a deconvolution. We present comprehensive results for the PSF estimation using our multispectral camera and provide deconvolution results.

A large number of multispectral cameras uses optical bandpass filters to divide the electromagnetic spectrum into passbands. If the filters are placed between the sensor and the lens, the different thicknesses, refraction indices and tilt angles of the filters cause image distortions, which are different for each spectral passband. On the other hand, the lens also causes distortions which are critical in machine vision tasks. In this paper, we propose a method to calibrate the multispectral camera geometrically to remove all kinds of geometric distortions. To this end, the combination of the camera with each of the bandpass filters is considered as single camera system. The systems are then calibrated by estimation of the intrinsic and extrinsic camera parameters and geometrically merged via a homography. The experimental results show that our algorithm can be used to compensate for the geometric distortions of the lens and the optical bandpass filters simultaneously.

High-fidelity color image acquisition with a multispectral camera utilizes optical filters to separate the visible electromagnetic spectrum into several passbands. This is often realized with a computer-controlled filter wheel, where each position is equipped with an optical bandpass filter. For each filter wheel position, a grayscale image is acquired and the passbands are finally combined to a multispectral image. However, the different optical properties and non-coplanar alignment of the filters cause image aberrations since the optical path is slightly different for each filter wheel position. As in a normal camera system, the lens causes additional wavelength-dependent image distortions called chromatic aberrations. When transforming the multispectral image with these aberrations into an RGB image, color fringes appear, and the image exhibits a pincushion or barrel distortion. In this paper, we address both the distortions caused by the lens and by the filters. Based on a physical model of the bandpass filters, we show that the aberrations caused by the filters can be modeled by displaced image planes. The lens distortions are modeled by an extended pinhole camera model, which results in a remaining mean calibration error of only 0.07 pixels. Using an absolute calibration target, we then geometrically calibrate each passband and compensate for both lens and filter distortions simultaneously. We show that both types of aberrations can be compensated and present detailed results on the remaining calibration errors.