Sensor yield is directly related to the probability of defective pixel occurrence and the screening criteria.
Assuming a spatially independent distribution of single pixel defects, effective on-the-fly correction of singlepixel
defects in a color plane, and effective correction of two-pixel defects in a color plane (couplets) through
a defect map, sensor yield can be computed based on the occurrence of three adjacent defective pixels in a
color plane (triplets). Closed-form equations are derived for calculating the probability of occurrence of
couplets and triplets as a function of the probability of a single pixel being defective. If a maximum of one
triplet is allowed in a 5-megapixel sensor, to obtain a 98% yield, the probability of a pixel being defective (p)
must not exceed 1.3E-3 (6500 defective pixels). For an 8-megapixel sensor, the corresponding requirement
would be p < 1.1E-3 (8900 defective pixels). Numerical simulation experiments have confirmed the accuracy
of the derived equations.
Due to the complex nature of hyper-spectra imaging, there are diversified noises in different bands of hyper-spectra
image. Without proper pre-processing, these noises will lead to false target detection results in application. Furthermore,
because of low signal to noise ratio, some bands, such as bands affected by water vapor in the infrared wavelengths,
cannot be utilized in the target detection task. To improve the performance of hyper-spectra applications, many noise
removal technologies have been developed. Most traditional denoising approaches either take only single band image
into account at a time or only consider spectra shape at one location a time. But these approaches could not deal
effectively with the common noises in hyper-spectra image that change from band to band and from one spatial spot to
another. Also most generalized smooth filters without local adaptation will lead to losses in spatial details at band
images. We propose a denoising approach that is based on bilateral filtering, which takes both spectra and spatial
information into account. By locally adapt to adjacent spectra distribution, this approach will have the advantage of
effective noise removal while keeping the spatial details in the band images. We also proposed parameter estimation
method for hyperspectral image bilateral filtering. The experiment results show that this approach deliver better
performance under various noises than other approach, the low signal to noise ratio in some band images have been
significantly improved.
A circle detection method utilizing Radon transform is proposed. In this paper, a closed form solution for the Radon
transform of a circular structure is derived from the Radon transform of a round disk. Because the Radon transform of
circle has the unique property of invariance to the angle change, a universal matched filter can be constructed from the
Radon transform of circle. To detect if there is a circular structure of specific radius presented in the image, a pre-defined
matched filter is applied to the Radon transform of the image at all angles and a circle presence intensity image
is reconstructed from the filtering results through filtered back projection (FBP). By thresholding the circle presence
intensity image, the presence and the location of the circle can be easily determined. The preliminary experimental
results show that the proposed method is effective and has better signal to noise ratio in output compared to the typical
Hough transform approach.
Hyperspectral image data can provide very fine spectral resolution with more than 200 bands, yet presents challenges for
visualization techniques for displaying such rich information on a tristimulus monitor. This study developed a
visualization technique by taking advantage of both the consistent natural appearance of a true color image and the
feature separation of a PCA image based on a biologically inspired visual attention model. The key part is to extract the
informative regions in the scene. The model takes into account human contrast sensitivity functions and generates a
topographic saliency map for both images. This is accomplished using a set of linear "center-surround" operations
simulating visual receptive fields as the difference between fine and coarse scales. A difference map between the
saliency map of the true color image and that of the PCA image is derived and used as a mask on the true color image to
select a small number of interesting locations where the PCA image has more salient features than available in the
visible bands. The resulting representations preserve hue for vegetation, water, road etc., while the selected attentional
locations may be analyzed by more advanced algorithms.
The problem of separating linear features from a textured background is of importance in many applications. It has been shown that the Fourier transform can be used in conjunction with polar transformation to "lift" linear features from the background texture. However, while the Fourier transform works well with lines that are spread throughout the entire image, it is less effective when the linear features are of varied length and thickness. We propose approaches based on a windowed Fourier transform and wavelet packet decomposition to lift randomly located lines of varied lengths and thickness. The reasoning underlying the development of the approaches is presented along with comparative examples.
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