In this paper, we investigate the potential application of the multispectral filter array (MSFA) techniques in multispectral imaging for reasons like low cost, exact registration, and strong robustness. In both human and many animal visual systems, different types of photoreceptors are organized into mosaic patterns. This behavior has been emulated in the industry to develop the so-called color filter array (CFA) in the manufacture of digital color cameras. In this way, only one color component is measured at each pixel, and the sensed image is a mosaic of different color bands. We extend this idea to multispectral imaging by developing generic mosaicking and demosaicking algorithms. The binary tree-driven MSFA design process guarantees that the pixel distributions of different spectral bands are uniform and highly correlated. These spatial features facilitate the design of the generic demosaicking algorithm based on the same binary tree, which considers three interrelated issues: band selection, pixel selection and interpolation. We evaluate the reconstructed images from two aspects: better reconstruction and better target classification. The experimental results demonstrate that the mosaicking and demosaicking process preserves the image quality effectively, which further supports that the MSFA technique is a feasible solution for multispectral cameras.
Mosaic technology has gained considerable popularity and has become mainstay in commercial digital color cameras. In a mosaicked sensor, each pixel detector is covered with a wavelength-specific optical filter. Since only one spectral band is sensed at each pixel, the other bands must be estimated from neighboring pixels. In the commercial digital color cameras, sophisticated algorithms have been developed to perform this estimation, based upon properties of the human visual system to minimize artifacts. To expand this technology for use in military applications such as missiles and smart ordnance, various modifications need to be made. Two of the bigger problems are the choice of spectral sensitivities for the filters and the pattern used in the mosaic. We propose a novel seven-band setup for multispectral imaging systems along with a choice of spectral sensitivities that "best" distinguish target from clutter in an information theoretic sense. These ideas are illustrated with hyperspectral visible/near-infrared images taken by the AVIRIS sensor. Following the image-capture stage, the display of these multispectral images on a three-channel display is also addressed.
Digital Still Color Cameras sample the visible spectrum using an array of color filters overlaid on a CCD such that each pixel samples only one color band. The resulting mosaic of color samples is processed to produce a high resolution color image such that a value of a color band not sampled at a certain location is estimated from its neighbors. This is often referred to as 'demosaicking.' In this paper, we approach the process of demosaicking as a bilateral filtering process which is a combination of spatial domain filtering and filtering based on similarity measures. Bilateral filtering smooths images while preserving edges by means of nonlinear combinations of neighboring image pixel values. A bilateral filter can enforce similarity metrics (such as squared error or error in the CIELAB space) between neighbors while performing the typical filtering operations. We have implemented a variety of kernel combinations while performing demosaicking. This approach provides us with a means to denoise, sharpen and demosaic the image simultaneously. We thus have the ability to represent demosaicking algorithms as spatial convolutions. The proposed method along with a variety of existing demosaicking strategies are run on synthetic images and real-world images for comparative purposes.
KEYWORDS: Video, Video processing, Field programmable gate arrays, Displays, Clocks, Analog electronics, Cameras, Semantic video, Signal processing, Human-machine interfaces
A video processing and display system for performing high speed geometrical image transformations has been designed. It involves looking up the video image by using a pointer memory. The system supports any video format which does not exceed the clock rate that the system supports. It also is capable of changing the brightness and colormap of the image through hardware.
This course explains the principles of displaying images and video using a Digital Micromirror Device (DMD) in UHP lamp and solid-state illuminated systems. The course will begin with giving the audience an overview of colorimetry as it applies to DLP-based systems and the various nuances of a creating a display system based on three to five color primaries. The audience will be given examples of single chip and three-chip systems walking them from electrical signals into the projector to photons out of the projector in both 2D and 3D displays. Examples will include three-chip systems used in DLP Cinema® projectors, color wheel- and lamp-based single chip projectors; LED- and LASER-based single chip projectors used in conventional projectors, televisions and Pico projectors.
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