In this paper, a full information vector recognition algorithm for moving targets is proposed on the basis of the characteristic distribution of point targets and the moving characteristic between frames. The traditional multi -frame image fusion method of moving target recognition is abandoned. We utilize the distribution characteristic of point targets extracted from single image and moving characteristic of point targets extracted from multiple images to recognize and classify moving targets with the similarity principle of feature vector. Compared with the traditional maximum likelihood estimation image processing algorithm, the proposed recognition method costs less computation and provides a novel approach for spatial moving target detection and recognition.
A new method for the lossless compression of the interferometer hyperspectral instrument Large Aperture Static Imaging Spectrometer (LASIS) data is presented in this paper. Differs from traditional hyperspectral instrument, the image captured by the two dimensional CCD detector of LASIS is no longer a normal image, but the two spatial information of the scene superimposed with interference fringes of equal thickness. There is a translation motion of the spatial information among each frame of LASIS data cube. Based on these unique data characteristics of LASIS and the recently presented CCSDS-123 lossless multispectral & Hyperspectral image compression standard, an improved predictor is designed for the prediction of LASIS data while using the standard. We perform several experiments on real data acquired by LASIS to investigate the performance of the proposed predictor. Experimental results show that the proposed predictor gives about 27.5% higher compression ratio than the default predictor of CCSDS-123 for lossless compression of LASIS data. In addition, the appropriate choice of several parameters of the proposed predictor are presented according to the experimental results.
Considering the images captured under hazy weather conditions are blurred, a new dehazing algorithm based on overlapped sub-block homomorphic filtering in HSV color space is proposed. Firstly, the hazy image is transformed from RGB to HSV color space. Secondly, the luminance component V is dealt with the overlapped sub-block homomorphic filtering. Finally, the processed image is converted from HSV to RGB color space once again. Then the dehazing images will be obtained. According to the established algorithm model, the dehazing images could be evaluated by six objective evaluation parameters including average value, standard deviation, entropy, average gradient, edge intensity and contrast. The experimental results show that this algorithm has good dehazing effect. It can not only improve degradation of the image, but also amplify the image details and enhance the contrast of the image effectively.
Scale and rotation invariant texture classification is a challenging and significant topic in texture analysis. This paper
presents a scale and rotation invariant Gabor Texture Descriptor (GTD) for texture classification. Firstly, Gabor filters
with different orientations and scales are carried out on an image. Secondly, to obtain a scale invariant GTD, different
scale energies of these Gabor filtered images are calculated, and the optimal matching scales of the GTD are chosen by
the sliding window filter with the scale energies. Thirdly, to extract a rotation invariant GTD, a new method using
Discrete Fourier Transform is employed on the scale invariant GTD. At last, the distances between the GTDs of a query
image and the target images are calculated for texture classification. Five different GTDs are employed for texture
classification to demonstrate the effectiveness of the proposed scale and rotation invariant GTD.
Motion-compensated three-dimensional embedded zeroblock coding (MC 3-D EZBC) is a successful state-of-the-art video compression algorithm. We propose a hyperspectral image compression coder based on the 3-D EZBC algorithm without motion compensation. This coder adopts the 3-D wavelet transform to decorrelate and the 3-D EZBC algorithm without motion compensation to process bitplane zeroblock coding. For achieving good coding performance, the diverse 3-D wavelet transform structures and the several wavelet filters are respectively compared and evaluated on the basis of floating-point lossy compression and lossless-to-lossy compression. We also study the problems of the optimal unitary scaling factors and list initialization order. Finally, the best choices were found for a given application, via the extensive experiments and analyses. Moreover, in comparison with several state-of-the-art wavelet coding algorithms, 3-D EZBC can provide better compression performance and unsupervised classification accuracy. Experimental results show that the average lossy compression performance (in floating-point mode and integer-based mode) of our coder respectively outperforms 3-D set partitioning in hierarchical trees (SPIHT) by 1.26 dB, 3-D set-partitioned embedded block (SPECK) by 0.68 dB, symmetric-tree (AT) 3-D SPIHT by 0.39 dB, and JPEG 2000-MC by 0.25 dB at 0.1 to 3.0 bits per pixel per band, and the lossless coding performance of 3-D EZBC is about 5% to 7% better than that of 3-D SPECK, 3-D SPIHT, and AT 3-D SPIHT. So the 3-D EZBC algorithm is also a good candidate to compress hyperspectral images.
KEYWORDS: 3D image processing, Hyperspectral imaging, Image compression, Lithium, Wavelets, Wavelet transforms, 3D modeling, Signal to noise ratio, Discrete wavelet transforms, Communication engineering
In this paper, a three-dimensional Set Partitioned Embedded Zero Block Coding (3D SPEZBC) algorithm for
hyperspectral image compression is proposed, which is motivated by the EZBC and SPECK algorithms. Experimental
results show that the 3D SPEZBC algorithm obviously outperforms 3D SPECK, 3D SPIHT and AT-3D SPIHT, and is
slightly better than JPEG2000-MC in the compression performances. Moreover, the 3D SPEZBC algorithm can save
considerable memory requirement in comparison with 3D EZBC.
By looking up pixels of image sequence as moving gas molecules, a novel concept, image temperature, has been proposed to describe the natural property of the image motion. The idea comes from the reveal of Maxwell-Boltaman Distribution Law in gas dynamic theories. Further, we develop another concept of energy flow corresponding to optical flow. Based on the concepts and the method of optical flow analysis, we developed the method of the Energy Flow Equation (EFE) to estimate image motion. Experiment indicates better performance of the proposed EFE scheme with significantly reduced false motion estimates than the traditional Optical Flow Equation does.
In industrial Radiogram, scatter noise, uneven distribution of X ray and other noises directly affect image quality. Among them scatter noise is the major part. Many researchers have employed various methods to make model to get accurate estimation of scatter distribution. But these methods and models are difficult to perform well in other different situations. In this paper we propose a method integrated physical and mathematical processing to solve this problem perfectly. Based on the analysis of the scatter distribution in X-ray image and the contribution of the low frequency noise to image deterioration, in this paper a new integrated method is used to correct these effects as follows. First using the optical theory we deduce the expression of scatter distribution, which is proved to be subject to Gaussian distribution. The expression provides a theoretic basis for removing the scatter with physical method, by which we get remarkable quality-improved X-ray images. Then multi- resolution analysis is employed to decompose the images acquired on the above theoretic basis, and it makes the processing of different frequency components of the X-ray images become easy. In our experiment, the results are satisfied. The method not only avoids making different models under different experiment conditions universally, but also provides a promising way for real-time X-ray image correction and detection.
KEYWORDS: Filtering (signal processing), Wavelet transforms, Wavelets, Deconvolution, Signal processing, Electronic filtering, Digital filtering, Signal to noise ratio, Data modeling, Linear filtering
A new approach of adaptive Kalman filtering deconvolution (AKFD) is developed basing on dyadic wavelet transform. The technique discards the assumption of stationarity for signals in predictive deconvolution, and overcomes improving resolution at the price of decreasing signal-to-noise (SNR) obviously. The technique can well compress the reflection waveforms, but the noises are not variable in substance. So it has a better ability of resistance noise. Suppression false reflections in dyadic wavelet transform domain is better than by applying AKFD in time domain. In addition the technique also has the characteristic of adaptive Kalman filter in every band for a signal respectively, it enhances the adaptation of Kalman filtering, so the resolution is obvious higher than that one in time domain. A great deal of numerical models and real seismic data indicate that the technique has obvious effect. At the same time, the technique also overcomes the drawback of increasing the low- frequency component of AKFD in time domain. A great deal of numerical models and real seismic data indicate that the technique has obvious effect. The approach not only suits for seismic data, but also can be used for reference to another similar signal processing.
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