Robust infrared small target detection in infrared warning and defending system is a challenging task due to the low signal-to-clutter ratios and complex background. Motivated by human vision system, we proposed a scale adaptive patch-based contrast measure(SPCM) method for infrared small target detection. At the first stage, a patch-based contrast model is established for measuring small target scale response, whose highest response corresponding to the best estimated size of the target, at the same time, filtering out some non-target areas and leave potential candidates. At the Second stage, we calculate local patch contrast at candidate regions with the estimated target scale. Utilizing the just right scale, the patch-based contrast measure could effectively suppress background clutter and extract infrared small target in single image. Finally, an improved adaptive threshold method by using statistical information of candidate target is used to segment infrared small target. In order to verify the effectiveness of the proposed approach, we compared our method with several fixed-scale and multi-scale infrared small detection algorithm. Experimental results indicate that our method is not only able to effectively estimate the actual scale of the target, but also detect weak small target accurately in heterogeneous background with low and comparable false alarm ratio, while achieving three times faster runtime performance than multi-scale algorithm.
With the development of technology, especially the rapid development of hand-held devices, it is more convenient to obtain video sequences, but the video quality still suffers from some issues, such as unwanted camera shakes and jitter. To address the issues, video stabilization techniques have been developed to obtain high quality and stable videos. Considering computational complexity and real-time requirements, patch matching, has become an important method for motion estimation and video stabilization. It transforms the video stabilization task into a minimum optimization problem. In this paper, we propose a novel patch matching method integrated with fireworks algorithm[1] for motion search, which is a novel swarm intelligence optimization algorithm. Inspired by the fireworks explode in the air, the established mathematical model can be formulated as a parallel explosive search method by introducing random factors and selection strategies, and thus developed into a global probability search method for solving the optimal solution of complex optimization problems. It has excellent performance and high efficiency in solving complex optimization problems. Experimental results show that the improved patch matching method based on fireworks algorithm has achieved better results, compared with the ones with traditional motion search algorithms.
Bidimensional convolution is a low-level processing algorithm of interest in many areas, but its high computational cost constrains the size of the kernels, especially in real-time embedded systems. This paper presents a hardware architecture for the ASIC-based implementation of 2-D convolution with medium–large kernels. Aiming to improve the efficiency of storage resources on-chip, reducing off-chip bandwidth of these two issues, proposed construction of a data cache reuse. Multi-block SPRAM to cross cached images and the on-chip ping-pong operation takes full advantage of the data convolution calculation reuse, design a new ASIC data scheduling scheme and overall architecture. Experimental results show that the structure can achieve 40× 32 size of template real-time convolution operations, and improve the utilization of on-chip memory bandwidth and on-chip memory resources, the experimental results show that the structure satisfies the conditions to maximize data throughput output , reducing the need for off-chip memory bandwidth.
This paper proposes a low-cost FPGA architecture of Speed-Up Robust Features (SURF) algorithm based on OpenSURF. It optimizes the computing architecture for the steps of feature detection and feature description involved in SURF to reduce the resource utilization and improve processing speed. As a result, this architecture can detect feature and extract descriptor from video streams of 800x600 resolutions at 60 frames per second (60fps). Extensive experiments have demonstrated its efficiency and effectiveness.
Passive millimeter waves (PMMW) image can create interpretable imagery on the objects concealed under clothing, which gives the great advantage to the security system. In conventional detection methods, the object detection methods can be roughly divided into two categories: the edge-based method and the region-based method. In this paper, we propose to combine the two methods for better detecting the concealed object for PMMW imaging. The main idea of the proposed method is to combine the edge-based contrast and region-based center-surround histogram. The proposed method can describe a concealed object locally and regionally, which help us capture more useful information about the edge and region. Experimental results on real images demonstrate that the proposed method can effectively detect the concealed object in the PMMW images.
Deconvolving Poissonian image has been a significant subject in various application areas such as astronomical, microscopic, and medical imaging. In this paper, a regularization-based approach is proposed to solve Poissonian image deconvolution by minimizing the regularization energy functional, which is composed of the generalized Kullback-Leibler divergence as the data-fidelity term and sparsity prior constraints as the regularization term, and a non-negativity constraint. We consider two sparsity prior constraints which include framelet-based analysis prior and combination of framelet and total variation analysis priors. Furthermore, we show that the resulting minimization problems can be efficiently solved by the split Bregman method. The comparative experimental results including quantitative and qualitative analysis manifest that our algorithm can effectively remove blur, suppress noise, and reduce artifacts.
A robust and fast Hausdorff distance (HD) method is presented for image matching. Canny edge operator is used for extracting edge points. HD measure is one of efficient measures for comparing two edge images by calculating the interpixel distance between two sets of edge points, and does not require the point-to-point correspondence. However, high computational complexity is a common problem for HD measure because a large number of edge points could be extracted used to calculate HD. Further, a great many incorrect edge points will be extracted under the condition of occlusion and other ill conditions. A gradient orientation selectivity strategy is proposed to not only select available edges, but also reduce the number of edge points. Experimental results show that the proposed method has less computational cost, and has good robustness for object matching, especially under partial occlusion and other ill conditions.
This paper deals with deconvolution problem for passive millimeter wave images with poor resolution and low SNR. A
passive millimeter wave images super-resolution algorithm based on semi-blind deconvolution is put forward. The
proposed method is based on two characteristic of imaging system. First, the PSF of imaging system is certain, can be
modeled by parametric function. Second, the noise imposes different influence degree on the low frequency and high
frequency parts of the pass-band of the image, the low frequency and high frequency part have high and low SNR,
respectively.The image is decomposed using the bilateral filtering into low frequency base layer and high frequency
detail layer. The base layer contains the large-scale structures and nearly frees with noise thus has the higher SNR,
whereas the detail layer includes both small-scale details and noise and has the lower SNR. The base layer is restored by
semi-blind deconvolution. The system PSF is modeled as a parametric Gaussian form. Edge structures information of the
image is extracted basing on Mumford-Shah model and used to adjust the regularization term adaptively, and iterative
method is used to estimate image and blurred kernel parameters. The detail layer is adaptively denoised by combining
the joint bilateral filtering method and with edge preservation in the guidance of the base layer. Finally, the high
resolution image is obtained by combining the base and detail layers. Comparative experimental results show that the
proposed method can effectively suppress noise, reduce artifacts, and improve the spatial resolution.
This paper deals with edge-preserving interpolation for passive millimeter wave images with poor resolution and low
SNR. A hybrid interpolation method basing on image decomposition via bilateral filtering is proposed. The low
resolution and noisy image is first decomposed using the bilateral filter into the base and detail layers which represent
large and small scale features, respectively. The base layer contains the large-scale structures and nearly frees with noise
thus has the higher SNR, whereas the detail layer includes both small-scale details and noise and has the lower SNR. The
detail layer is adaptively denoised by the joint bilateral filtering method and subsequently interpolated with edge
preservation in the guidance of the base layer. The base layer is interpolated with edge-preserving method directly.
Finally, the high resolution image is obtained by combining the base and detail layers. Experimental results show that the
proposed method outperforms the conventional methods while preserving edges and suppressing jagging thus is suited
for PMMW images interpolation to enhance resolution.
A new blur identification and restoration method is presented. We observe that blurring increases the second-order
central moment (SOCM) of image and introduce a new parametric blur identification method by minimizing SOCM. The
method applied to finite support images, in which the scene consists of a finite extent object against a uniformly black,
grey or white background. The method has been validated by direct comparisons with other methods on simulated
images. Our experiments show that the SOCM minimization measurements match well with methods than maximize
PSNR.
KEYWORDS: Digital signal processing, Field programmable gate arrays, Image processing, Parallel processing, Signal processing, Data processing, Deconvolution, Detection and tracking algorithms, Image restoration, Point spread functions
In this paper, we present a co-design method for parallel image processing accelerator based on DSP and FPGA. DSP is
used as application and operation subsystem to execute the complex operations, and in which the algorithms are
resolving into commands. FPGA is used as co-processing subsystem for regular data-parallel processing, and operation
commands and image data are transmitted to FPGA for processing acceleration. A series of experiments have been
carried out, and up to a half or three quarter time is saved which supports that the proposed accelerator will consume less
time and get better performance than the traditional systems.
An improved algorithm integrating wavelet decomposition, multilevel filtering, and an additive operator splitting (AOS)-based level-set framework for infrared small target detection is proposed. This model has two components: a filtering operation, and level-set evolution. In the filtering step, the original image is first decomposed using a wavelet transform. After determining the location of sea-sky line, we construct a subimage based on the sea-sky-line position, and then execute multilevel filtering on this subimage. This filtering framework provides the input image for the level-set evolution. Using the level-set formulation, complex curves can be detected while naturally handling topological changes of the evolving curves. To reduce the computational cost required by an explicit implementation of the level-set formulation, a new solver named AOS is proposed. Additionally, the quantitative analyses for our algorithm are also given. Experiments on real infrared image sequences indicate that our method is efficient and robust.
A clutter suppression algorithm called the rectification filter with indications of bidirectional local binary patterns (BDLBP-RF) is proposed as a resolution to the problem of detecting dim targets in infrared (IR) image sequences. First of all, a local binary pattern (LBP) operator with properties of grayscale and rotation invariance is introduced in the application of clutter suppression. Each pixel in the image is estimated by its spatial neighbor pixels and the corresponding LBPs in prior and posterior frames. The approach proposed is based on a spatiotemporal process, in which interframe and intraframe properties of the IR image sequence are both taken into account. The method is evaluated by the comparative experiments, and the LBP operator's optimum values of radius and number of neighbors are discussed. The results of the experiment prove that BDLBP-RF has excellent performance and stability in clutter suppression under various situations. The target point in images processed by our approach received high signal-to-clutter ratio gain, and the detectability of the target is enhanced.
The restoration of rotational motion blurred image involves a lot of interpolations operators in rectangular-to-polar
transformation and its inversion of polar-to-rectangular. The technique of interpolation determines the quality of
restoration and computational complexity. In this paper, we incorporate orthogonal chebyshev polynomials interpolations
into the processing of restoration of rotational motion blurred image, in which the space-variant blurs are decomposed
into a series of space-invariant blurs along the blurring paths, and the blurred gray-values of the discrete pixels of the
blurring paths are calculated by using of orthogonal chebyshev polynomials' interpolations and the space-variant blurs
can be removed along the blurring paths in the polar system. At same way, we use orthogonal chebyshev polynomials'
interpolations to perform polar-to-rectangular transformation to put the restored image back to its original rectangular
format. In order to overcome the interference of noise, an optimization restoration algorithm based on regularizations is
presented, in which non-negative and edge-preserving smoothing are incorporated into the process of restoration. A
series of experiments have been performed to test the proposed interpolation method, which show that the proposed
interpolations are effective to preserve edges.
KEYWORDS: Anisotropic diffusion, Gaussian filters, Smoothing, Image filtering, Diffusion, Linear filtering, Signal to noise ratio, Digital filtering, Denoising, Performance modeling
In each step of anisotropic diffusion smoothing, noises must be managed to get better results. The mostly used method is
Gaussian filtering. However, the standard deviation of the Gaussian filter can't be accurately obtained and it should
change during the iterative process. Another problem is how to select a proper standard deviation to reducing noises
while preserving edges. Actually, facet model fitting can be taken as a natural way to overcome the drawbacks
mentioned above. Facet model fitting has the low-pass filtering performance adaptive to the image during evolution of
diffusion; it can also achieve balanced results for noise reduction and edge preserving. Experiments show the method can
preserve more edges as well as obtain higher peak signal-to-noise ratio as compared to other anisotropic diffusion based
selective smoothing approaches.
We propose a fast method for linearizing the edge-preserving regularization. The regularization operator is decomposed to the sum of some linear operators. The phase-only image is used in place of the estimated image. Those linear operators are computed from the phase-only image. They are the approximation of the true regularization operator. The new discontinuity maps do not need to be computed from the last image estimate at every step of the algorithm. This fast method can reduce much algorithm processing time.
KEYWORDS: Digital signal processing, Telecommunications, Video, Control systems, Real time image processing, Data communications, Image processing, Computing systems, Multiplexers, Video processing
Building Multi-DSPs system is an effective way to elevate the system processing ability. In this paper, a VXI-based dual-bus multi-DSPs real-time image processing system is presented. With VXI, the system becomes modularity, easy to modify and extend. At the same time, specified bi-directional high-speed bus groups are adopted to overcome the efficiency rebate produced by bandwidth limit of VXIbus. Performance of this system compared with some other realization is provided at the end of the article.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.