Johannes Brauers

Bio: Johannes Brauers is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: Multispectral image & Optical filter. The author has an hindex of 11, co-authored 23 publications receiving 356 citations.

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

Abstract: 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.

Direct PSF Estimation Using a Random Noise Target

Abstract: 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.

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

Abstract: 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.

Multispectral high dynamic range imaging

Abstract: 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.© (2008) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Turbo decodulation: iterative combined demodulation and source-channel decoding

Abstract: 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.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

FlexISP: a flexible camera image processing framework

Abstract: Conventional pipelines for capturing, displaying, and storing images are usually defined as a series of cascaded modules, each responsible for addressing a particular problem. While this divide-and-conquer approach offers many benefits, it also introduces a cumulative error, as each step in the pipeline only considers the output of the previous step, not the original sensor data. We propose an end-to-end system that is aware of the camera and image model, enforces natural-image priors, while jointly accounting for common image processing steps like demosaicking, denoising, deconvolution, and so forth, all directly in a given output representation (e.g., YUV, DCT). Our system is flexible and we demonstrate it on regular Bayer images as well as images from custom sensors. In all cases, we achieve large improvements in image quality and signal reconstruction compared to state-of-the-art techniques. Finally, we show that our approach is capable of very efficiently handling high-resolution images, making even mobile implementations feasible.

Near-Capacity Multi-Functional MIMO Systems: Sphere-Packing, Iterative Detection and Cooperation

Abstract: About the Authors. OtherWiley IEEE Press Books on Related Topics. Preface. Acknowledgments. 1 Problem Formulation, Objectives and Benefits. 1.1 TheWireless Channel and the Concept of Diversity. 1.2 Diversity and Multiplexing Trade-offs in Multi-functional MIMO Systems. 1.3 Coherent versus Non-coherent Detection for STBCs Using Co-located and Cooperative Antenna Elements. 1.4 Historical Perspective and State-of-the-Art Contributions. 1.5 Iterative Detection Schemes and their Convergence Analysis. 1.6 Outline and Novel Aspects of the Monograph. Part I Coherent Versus Differential Turbo Detection of Sphere-packing-aided Single-user MIMO Systems. List of Symbols in Part I. 2 Space-Time Block Code Design using Sphere Packing. 2.1 Introduction. 2.2 Design Criteria for Space-Time Signals. 2.3 Design Criteria for Time-correlated Fading Channels. 2.4 Orthogonal Space-Time Code Design using SP. 2.5 STBC-SP Performance. 2.6 Chapter Conclusions. 2.7 Chapter Summary. 3 Turbo Detection of Channel-coded STBC-SP Schemes. 3.1 Introduction. 3.2 System Overview. 3.3 Iterative Demapping. 3.4 Binary EXIT Chart Analysis. 3.5 Performance of Turbo-detected Bit-based STBC-SP Schemes. 3.6 Chapter Conclusions. 3.7 Chapter Summary. 4 Turbo Detection of Channel-coded DSTBC-SP Schemes. 4.1 Introduction. 4.2 Differential STBC using SP Modulation. 4.3 Bit-based RSC-coded Turbo-detected DSTBC-SP Scheme. 4.4 Chapter Conclusions. 4.5 Chapter Summary. 5 Three-stage Turbo-detected STBC-SP Schemes. 5.1 Introduction. 5.2 System Overview. 5.3 EXIT Chart Analysis. 5.4 Maximum Achievable Bandwidth Efficiency. 5.5 Performance of Three-stageTurbo-detected STBC-SP Schemes. 5.6 Chapter Conclusions. 5.7 Chapter Summary. 6 Symbol-based Channel-coded STBC-SP Schemes. 6.1 Introduction. 6.2 System Overview. 6.3 Symbol-based Iterative Decoding. 6.4 Non-binary EXIT Chart Analysis. 6.5 Performance of Bit-based and Symbol-based LDPC-coded STBC-SP Schemes. 6.6 Chapter Conclusions. 6.7 Chapter Summary. Part II Coherent Versus Differential Turbo Detection of Single-user and Cooperative MIMOs. List of Symbols in Part II. 7 Linear Dispersion Codes: An EXIT Chart Perspective. 7.1 Introduction and Outline. 7.2 Linear Dispersion Codes. 7.3 Link Between STBCs and LDCs. 7.4 EXIT-chart-based Design of LDCs. 7.5 EXIT-chart-based Design of IR-PLDCs. 7.6 Conclusion. 8 Differential Space-Time Block Codes: A Universal Approach. 8.1 Introduction and Outline. 8.2 System Model. 8.3 DOSTBCs. 8.4 DLDCs. 8.5 RSC-coded Precoder-aided DOSTBCs. 8.6 IRCC-coded Precoder-aided DLDCs. 8.7 Conclusion. 9 Cooperative Space-Time Block Codes. 9.1 Introduction and Outline. 9.2 Twin-layer CLDCs. 9.3 IRCC-coded Precoder-aided CLDCs. 9.4 Conclusion. Part III Differential Turbo Detection of Multi-functional MIMO-aided Multi-user and Cooperative Systems. List of Symbols in Part III. 10 Differential Space-Time Spreading. 10.1 Introduction. 10.2 DPSK. 10.3 DSTS Designusing Two Transmit Antennas. 10.4 DSTS Design Using Four Transmit Antennas. 10.5 Chapter Conclusions. 10.6 Chapter Summary. 11 Iterative Detection of Channel-coded DSTS Schemes. 11.1 Introduction. 11.2 Iterative Detection of RSC-coded DSTS Schemes. 11.3 Iterative Detection of RSC-coded and Unity-rate Precoded Four-antenna-aided DSTS-SP System. 11.4 Chapter Conclusions. 11.5 Chapter Summary. 12 Adaptive DSTS-assisted Iteratively Detected SP Modulation. 12.1 Introduction. 12.2 System Overview. 12.3 Adaptive DSTS-assisted SP Modulation. 12.4 VSF-based Adaptive Rate DSTS. 12.5 Variable-code-rate Iteratively Detected DSTS-SP System. 12.6 Results and Discussion. 12.7 Chapter Conclusion and Summary. 13 Layered Steered Space-Time Codes. 13.1 Introduction. 13.2 LSSTCs. 13.3 Capacity of LSSTCs. 13.4 Iterative Detection and EXIT Chart Analysis. 13.5 Results and Discussion. 13.6 Chapter Conclusions. 13.7 Chapter Summary. 14 DL LSSTS-aided Generalized MC DS-CDMA. 14.1 Introduction. 14.2 LSSTS-aided Generalized MCDS-CDMA. 14.3 Increasing the Number of Users by Employing TD and FD Spreading. 14.4 Iterative Detection and EXIT Chart Analysis. 14.5 Results and Discussion. 14.6 Chapter Conclusions. 14.7 Chapter Summary. 15 Distributed Turbo Coding. 15.1 Introduction. 15.2 Background of Cooperative Communications. 15.3 DTC. 15.4 Results and Discussion. 15.5 Chapter Conclusions. 15.6 Chapter Summary. 16 Conclusions and Future Research. 16.1 Summary and Conclusions. 16.2 Future Research Ideas. 16.3 Closing Remarks. A Gray Mapping and AGM Schemes for SP Modulation of Size L =16. B EXIT Charts of Various Bit-based Turbo-detected STBC-SP Schemes. C EXIT Charts of Various Bit-based Turbo-detected DSTBC-SP Schemes. D LDCs' / for QPSK Modulation. E DLDCs' / for 2PAM Modulation. F CLDCs' / 1 and / 2 for BPSK Modulation. G Weighting Coefficient Vectors e and a. H Gray Mapping and AGM Schemes for SP Modulation of Size L =16. Glossary. Bibliography. Index. Author Index.