Johannes Brauers

Multispectral Filter-Wheel Cameras: Geometric Distortion Model and Compensation Algorithms

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.

High Dynamic Range Microscopy for Cytopathological Cancer Diagnosis

Cancer is one of the most common causes of death. Cytopathological, i.e., cell-based, diagnosis of cancer can be applied in screening scenarios and allows an early and highly sensitive detection of cancer, thus increasing the chance for cure. The detection of cancer on cells addressed in this paper is based on bright field light microscopy. The cells are imaged with a camera mounted on a microscope, allowing to measure cell properties. However, these cameras exhibit only a limited dynamic range, which often makes the quantification of properties difficult or even impossible. Consequently, to allow a computer-assisted analysis of microscopy images, the imaging has to be improved. To this end, we show how the dynamic range can be increased by acquiring a set of differently exposed cell images. These high dynamic range (HDR) images allow to measure cellular features that are otherwise difficult to capture, if at all. We show that HDR microscopy not only increases the dynamic range, but furthermore reduces noise and improves the acquisition of colors. We develop HDR microscopy-based algorithms, which are essential for cytopathological oncology and early cancer detection and only possible with HDR microscopy imaging. We show the detection of certain subcellular features, so-called AgNORs, in silver (Ag) stained specimens. Furthermore, we give examples of two further applications, namely: 1) the detection of stained cells in immunocytochemical preparations and 2) color separation for nuclear segmentation of specimens stained with low contrast.

Turbo decodulation: iterative combined demodulation and source-channel decoding

We propose the combination of iterative demodulation and iterative source-channel decoding as a multiple turbo process. The receiver structures of bit-interleaved coded modulation with iterative decoding (BICM-ID), iterative source-channel decoding (ISCD), and iterative source coded modulation (ISCM) are merged to one novel turbo system, in which in two iterative loops reliability information is exchanged between the three single components, demodulator, channel decoder and (softbit) source decoder. Simulations show quality improvements compared to the different previously known systems, which use iterative processing only for two components of the receiver.

Multispectral high dynamic range imaging

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 imaging1, 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.

Geometric Calibration of Lens and Filter Distortions for Multispectral Filter-Wheel Cameras

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.

The EXIT-characteristic of softbit-source decoders

We outline a new technique to compute the EXIT-characteristic of softbit-source decoders analytically without extensive histogram measurements. Based on the analytic considerations it is straightforward to derive a compact determination rule for the maximum value of attainable extrinsic information. We also show that the area under the EXIT-characteristic grows almost logarithmically with the prediction gain which is utilizable due to the residual redundancy in the source data.

Longitudinal aberrations caused by optical filters and their compensation in multispectral imaging

Multispectral cameras enable high-fidelity color image acquisition by separation of the electromagnetic spectrum with optical bandpass filters mounted on a filter wheel between optics and imaging sensor. A multispectral image is acquired by capturing one grayscale image per wheel position and combining the images afterwards. Since the optical filters differ in their thicknesses and refraction indices, both transversal (geometric distortions) and longitudinal aberrations (blurring) occur. We present a physical model for the longitudinal aberrations and describe the resulting distortion effects. This knowledge serves as a basis for our compensation algorithm, which performs a shift-variant deconvolution of the image, significantly improving the image quality. We furthermore describe a practicable calibration procedure for our algorithm.

Minimum Terms of Residual Redundancy for Successful Iterative Source-Channel Decoding

Iterative source-channel decoding (ISCD) improves the error robustness of a digital communication system by iteratively evaluating natural residual source redundancy and artificial channel coding redundancy in a TURBO-like process. Based on recent results to extrinsic information transfer (EXIT) charts we present a novel (experimental) approach to quantify the minimum terms of residual redundancy which are needed for (almost) successful ISCD. Moreover, we clarify why in certain situations the decoding trajectory exceeds the EXIT-characteristic of soft decision source decoding (SDSD) in an ISCD scheme

Modeling and Compensation of Ghosting in Multispectral Filter Wheel Cameras

Multispectral filter wheel cameras divide the visible electromagnetic spectrum by using several optical bandpass filters mounted on a filter wheel and acquire one color component for each filter wheel position. Afterwards, the single images are combined into one multispectral image. While the color accuracy of this approach and the stop-band attenuation of the bandpass filters is superior to other technologies, ghosting images are produced by reflections between the image sensor and the filter surface: The original image is duplicated in a displaced, weaker and softened form and added to the original image, thus compromising the original. We analyze the path of rays in this specific optical setup and derive a physical model for the ghosting reflections. By linking the physical model to the image content, we derive a calibration and compensation algorithm, whose parameters are estimated from a test image. Application of our correction algorithm makes the ghosting virtually vanish.

Methods for spectral characterization of multispectral cameras

High fidelity color image acquisition requires an accurate characterization of the cameras spectral sensitivity curves to perform color calibration or spectral estimation. Several methods have been proposed to perform this task; these include characterizations via test charts, narrowband filters and methods utilizing a monochromator. In most publications, RGB cameras are characterized. In this paper, we describe the characterization of the spectral sensitivity curves of a multispectral camera featuring seven optical bandpass filters. We show two different methods for the calibration using a monochromator - either by measuring the grayscale sensor of the camera and the filters separately or by characterizing the multispectral camera as a complete system. A comparison of both methods validates the measurement results. We furthermore develop different reconstruction methods (maximum value method, principal eigenvector method, linear or Wiener estimation). We perform also simulations of the characterization process to evaluate the methods and show the impact of the bandwidth of the monochromator stimuli on the reconstruction.