A new effective algorithm of impulse noise suppression is proposed. First an iterative procedure composes a helper function associated with the filtered image, then this helper function allows realizing efficient impu...
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A new effective algorithm of impulse noise suppression is proposed. First an iterative procedure composes a helper function associated with the filtered image, then this helper function allows realizing efficient impulse detection for the conventional two steps filtering scheme. In our previous papers we suggested a simple adaptive algorithm of impulse noise detection in monochrome images that takes into account the size of signal gradient neighborhoods and image statistics. In this work the detection scheme is noticeably ameliorated. Further investigations shown that the proposed modification of the gradient based impulse detector highly improves the results of the filtering in terms of both subjective and objective criteria.
In this paper, we present a new fusion algorithm based on a multidecomposition approach with the DFT based symmetric, zero-phase, nonoverlapping digital filter bank representation. The DFT of the signal is separated i...
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In this paper, we present a new fusion algorithm based on a multidecomposition approach with the DFT based symmetric, zero-phase, nonoverlapping digital filter bank representation. The DFT of the signal is separated into two parts leading to the low and high -pass components then decimated by two to obtain subband signals. The original signal may be recovered by interpolating the subband signals, computing their inverse DFT and summing the results. In the proposed image fusion algorithm, two or more source images are decomposed into subbands by DFT based digital filters. The detail and approximation subband coefficients are modified according to their magnitudes and mean values, respectively. Then, the modified subbands are combined in the subband domain. Finally, the fused image is obtained by the inverse transform.
作者：
AlZahir, SUNBC
Dept Comp Sci Image Proc Graph & Multimedia Lab Prince George BC V2N 4Z9 Canada
In a numerous imageapplications, image resizing is becoming indispensable as it impacts the bandwidth and storage requirements tremendously. image resizing process introduces (or eliminates) many pixels to (or from) ...
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ISBN:
(纸本)0780388380
In a numerous imageapplications, image resizing is becoming indispensable as it impacts the bandwidth and storage requirements tremendously. image resizing process introduces (or eliminates) many pixels to (or from) the original image. The values of such pixels and their positions must be carefully determined otherwise visible distortion and significant degradation in the quality of the image may occur. Several methods have been employed to resize images including pixel replication;linear interpolation;higher order interpolations, Beizier methods, DCT-based, wavelets and others [1,2,3,4,5,6]. In this paper, we introduce a new perceptually perfect image-resizing scheme that near optimally preserves edges and highly maintains the quality of homogenous regions. In this technique, the image is segmented via an efficient edge detector to produce an edge image and independent homogenous regions. The edge image is resized separately from the homogenous regions via chain coding and elaborate look-ahead-and-back tables technique [4,5]. Homogenous regions are resized using a merciful adaptive region-based interpolation that exploits the characteristics of each region. At the end, the two parts are summed up to produce the desired resized image as shown in Figure 3. Simulation results of numerous test images show that the proposed technique is subjectively and objectively far better than published results.
Thermoacoustic tomography (TAT) is an emerging imaging technique with great potential for a wide range of biomedical imaging applications. It is customary in TAT to assume that the object is acoustically homogeneous, ...
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ISBN:
(纸本)0819457248
Thermoacoustic tomography (TAT) is an emerging imaging technique with great potential for a wide range of biomedical imaging applications. It is customary in TAT to assume that the object is acoustically homogeneous, which can result in image artifacts in medical applications. In this work, we investigate an iterative reconstruction approach for TAT that can compensate for acoustic heterogeneities via inversion of a generalized Radon transform imaging model. We demonstrate numerically that the generalized Radon transform model can be inverted uniquely and stably by use of only half of the acquired measurement data. The effects of imperfect knowledge of the acoustic heterogeneity map are also investigated.
The proceedings contain 93 papers. The topics discussed include: local adaptive image restoration and enhancement with the use of DFT and DCT in a running window;wavelet-based interpolation method for nonuniformly sam...
The proceedings contain 93 papers. The topics discussed include: local adaptive image restoration and enhancement with the use of DFT and DCT in a running window;wavelet-based interpolation method for nonuniformly sampled signals;regularization constraints in lossy compressed astronomical image restoration;lattice structure for multifilters derived from complex-valued scalar filter banks;multiwavelet-transform-based image compression techniques;construction of two-dimensional multiwavelets on a triangulation;determination of the scaling parameters of affine fractal interpolation functions with the aid of wavelet analysis;scaled Gabor representation: a refined time-frequency decomposition;Riesz frames and finite-dimensional approaches to problems in frame theory;integration-free projection of a sampled signal on a multiresolution analysis ladder space via approximation theory;and high-accuracy reconstruction from wavelet coefficients.
Within the scope of this paper a both compact and economical data acquisition system for multispecral images is described. It consists of a CCD camera, a liquid crystal tunable filter in combination with an associated...
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Within the scope of this paper a both compact and economical data acquisition system for multispecral images is described. It consists of a CCD camera, a liquid crystal tunable filter in combination with an associated concept for data processing. Despite of their limited functionality (e.g. regarding calibration) in comparison with commercial systems such as AVIRIS the use of these upcoming compact multispectral camera systems can be advantageous in many applications. Additional benefit can be derived adding online data processing. In order to maintain the systems low weight and price this work proposes to separate data acquisition and processing modules, and transmit pre-processed camera data online to a stationary high performance computer for further processing. The inevitable data transmission has to be optimised because of bandwidth limitations. All mentioned considerations hold especially for applications involving mini-unmanned-aerial-vehicles (mini-UAVs). Due to their limited internal payload the use of a lightweight, compact camera system is of particular importance. This work emphasises on the optimal software interface in between pre-processed data (from the camera system), transmitted data (regarding small bandwidth) and post-processed data (based on high performance computer). Discussed parameters are pre-processing algorithms, channel bandwidth, and resulting accuracy in the classification of multispectral image data. The benchmarked pre-processing algorithms include diagnostic statistics, test of internal determination coefficients as well as loss-free and lossy data compression methods. The resulting classification precision is computed in comparison to a classification performed with the original image dataset.
The high resolution imaging capability of Synthetic Aperture Radar (SAR) is largely unaffected by atmospheric conditions and has proven to be an indispensable asset in a variety of military and civilian applications. ...
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ISBN:
(纸本)0819457930
The high resolution imaging capability of Synthetic Aperture Radar (SAR) is largely unaffected by atmospheric conditions and has proven to be an indispensable asset in a variety of military and civilian applications. Application of SAR methodology for real-time imaging however carries with it the large computational complexity and storage requirements of the image-forming algorithms. Recently however, the rapidly diminishing cost of computing hardware and the related ascent of cluster-based computing, has made parallelization of these algorithms an appealing, area of investigation. This paper describes a parallel SAR processor developed at MIT Lincoln Laboratory. Several novel technologies were employed in it`s implementation, including pMatlab which is a parallel extension of standard Matlab that is also being developed at MIT Lincoln Laboratory. These technologies will be described later in the document. We begin with a brief description of the basic SAR algorithm.
Rate control for video transmission becomes extremely important in "bandwidth-precious" scenarios and added real-time constraints_such as joint source channel coding make it even more vital. Hence, there has...
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Rate control for video transmission becomes extremely important in "bandwidth-precious" scenarios and added real-time constraints_such as joint source channel coding make it even more vital. Hence, there has always been a demand for simple and efficient rate control algorithms. The approximate linear relationship between coding rate (R) and percentage of zeros among the quantized spatial transform coefficients (ρ) is exploited in the present work, to cater to such low-bandwidth, low-delay applications. The current rate control algorithm for H.264 is used as the benchmark for comparison. The extensive experimental results show that ρ -Domain model outperforms the existing algorithm with a more robust rate control, besides yielding a similar or improved Peak signal to Noise Ratio (PSNR) and being faster.
Target tracking in forward looking infrared (FLIR) video sequences is challenging problem due to various limitations such as low signal-to-noise ratio, image blurring, partial occlusion, and low texture information, w...
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Target tracking in forward looking infrared (FLIR) video sequences is challenging problem due to various limitations such as low signal-to-noise ratio, image blurring, partial occlusion, and low texture information, which often leads to missing targets or tracking non-target objects. To alleviate these problems, we propose the application of quadratic correlation filters using subframe approach in FLIR. The proposed filtering technique avoids the disadvantages of pixel-based image preprocessing techniques. The filter coefficients are obtained for desired target class from the training images. For real time applications, the input scene is first segmented to the subframes according to target location information from the previous frame. The subframe of interest is then correlated with correlation filters associated with target class. The obtained correlation output contains higher value that indicates the target location in the region of interest. The simulation results for target tracking in real life FLIR imagery have been reported to verify the effectiveness of the proposed technique.
The demand continues to grow for small, compact imaging sensors, which include new capabilities, such as response in multiple spectral bands, increased sensitivity, wide high dynamic range, and operating at room tempe...
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ISBN:
(纸本)081945768X
The demand continues to grow for small, compact imaging sensors, which include new capabilities, such as response in multiple spectral bands, increased sensitivity, wide high dynamic range, and operating at room temperature. These goals are dependant upon novel concepts in sensor technology, especially advanced electronic processing integrated with the sensor. On-focal plane processing is especially important to realize the full potential of the sensor. Since the area available for focal plane processing is extremely limited, a new paradigm in sensor electronic read-out technology is necessary to bridge the gap between multi-functional, high performance detector arrays and the off-focal plane processing. The Vertically Integrated Sensor Array (VISA) Program addresses this need through development of pixel-to-pixel interconnected silicon processors at the detector, thus expanding the area available for signal and imageprocessing. The VISA Program addresses not only the array interconnection technology, but also investigates circuit development adapted to this new three-dimensional focal plane architecture. This paper reviews progress in the first phase of the program and outlines direction for demonstrations of vertically integrated sensor arrays.
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