A classical problem of additive white (spatially uncorrelated) Gaussian noise suppression in grayscale images is considered. The main attention is paid to discrete cosine transform (DCT)-based denoising, in particular, to image processing in blocks of a limited size. The efficiency of DCT-based image filtering with hard thresholding is studied for different sizes of overlapped blocks. A multiscale approach that aggregates the outputs of DCT filters having different overlapped block sizes is proposed. Later, a two-stage denoising procedure that presumes the use of the multiscale DCT-based filtering with hard thresholding at the first stage and a multiscale Wiener DCT-based filtering at the second stage is proposed and tested. The efficiency of the proposed multiscale DCT-based filtering is compared to the state-of-the-art block-matching and three-dimensional filter. Next, the potentially reachable multiscale filtering efficiency in terms of output mean square error (MSE) is studied. The obtained results are of the same order as those obtained by Chatterjee's approach based on nonlocal patch processing. It is shown that the ideal Wiener DCT-based filter potential is usually higher when noise variance is high.
In many image-processing applications, observed images are contaminated by a nonstationary noise and no a priori information on noise dependence on local mean or about local properties of noise statistics is available. In order to remove such a noise, a locally adaptive filter has to be applied. We study a locally adaptive filter based on evaluation of image local activity in a "blind" manner and on discrete cosine transform computed in overlapping blocks. Two mechanisms of local adaptation are proposed and applied. The first mechanism takes into account local estimates of noise standard deviation while the second one exploits discrimination of homogeneous and heterogeneous image regions by adaptive threshold setting. The designed filter performance is tested for simulated data as well as for real-life remote-sensing and maritime radar images. Recommendations concerning filter parameter setting are provided. An area of applicability of the proposed filter is defined.
In various practical situations of remote sensing image processing it is assumed that noise is nonstationary and no a
priory information on noise dependence on local mean or about local properties of noise statistics is available. It is
shown that in such situations it is difficult to find a proper filter for effective image processing, i.e., for noise removal
with simultaneous edge/detail preservation. To deal with such images, a local adaptive filter based on discrete cosine
transform in overlapping blocks is proposed. A threshold is set locally based on a noise standard deviation estimate
obtained for each block. Several other operations to improve performance of the locally adaptive filter are proposed and
studied. The designed filter effectiveness is demonstrated for simulated data as well as for real life radar remote sensing
and marine polarimetric radar images.
A solution for the problem of nonsupervized recognition in the conditions of a priori indefinite number of object classes in radar images is presented. The designed algorithm performs image clustering to divide image objects into classes. The region of interest is can be chosen by user and then probabilistic filtering is applied to recognize the objects of the predetermined class on the entire image. The algorithm is operated on the multichannel data and shows stable recognition results.
A new algorithm of locally adaptive wavelet transform based on the modified lifting scheme is presented. It performs an adaptation of the wavelet high-pass filter at the prediction stage to the local image data activity. The proposed algorithm uses the generalized framework for the lifting scheme that permits to obtain easily different wavelet filter coefficients in the case of the (~N, N) lifting. Changing wavelet filter order and different control parameters, one can obtain the desired filter frequency response. It is proposed to perform the hard switching between different wavelet lifting filter outputs according to the local data activity estimate. The proposed adaptive transform possesses a good energy compaction. The designed algorithm was tested on different images. The obtained simulation results show that the visual and quantitative quality of the restored images is high. The distortions are less in the vicinity of high spatial activity details comparing to the non-adaptive transform, which introduces ringing artifacts. The designed algorithm can be used for lossy image compression and in the noise suppression applications.
The paper presents a new method of probabilistic filtering for radar target recognition. The classical Bayesian detector/estimator suffers from the insufficient information about target signature probability distributions and their a priory appearance probabilities. If the number of radar image objects to be classified is not known exactly the appeared unknown target may be wrong classified as one of the known targets. To eliminate this type of errors one can use the known probabilistic windows matched by shape to the recognition signature distributions. The combination of the probability window with a non-linear transform of the signature space is proposed in the paper. Such a combination forms a probabilistic filter. The probabilistic filter output is proportional to the likelihood probability of how the sensed object matches to its statistical model. The theoretical background of the probabilistic filtering method and its application to real X-band radar data are presented in the paper. The proposed method reduces the amount of a priory information required for the recognition and detects well the objects of the same nature independently from their size. For example, the probabilistic filter classifies well the different type of vegetation in the radar images.
A new algorithm of locally adaptive wavelet transform is presented. The algorithm implements the integer-to-integer lifting scheme. It performs an adaptation of the wavelet function at the prediction stage to the local image data activity. The proposed algorithm is based on the generalized framework for the lifting scheme that permits to obtain easily different wavelet coefficients in the case of the (N~,N) lifting. It is proposed to perform the hard switching between (2, 4) and (4, 4) lifting filter outputs according to an estimate of the local data activity. When the data activity is high, i.e., in the vicinity of edges, the (4, 4) lifting is performed. Otherwise, in the plain areas, the (2,4) decomposition coefficients are calculated. The calculations are rather simples that permit the implementation of the designed algorithm in fixed point DSP processors. The proposed adaptive transform possesses the perfect restoration of the processed data and possesses good energy compactation. The designed algorithm was tested on different images. The proposed adaptive transform algorithm can be used for
image/signal lossless compression.
The invariant signatures of a polarimetric radar are considered.
It is established theoretically that the polarimetric characteristics
of the backscattered radar signals depend on the shape and the electrophysical parameters of the sensed objects. This statement is confirmed by the experimental data. The presented polarimetric diagrams of the backscattered signals of the vertical and horizontal polarizations reflected from different sensed objects show the difference in the polarimetric invariant signatures. The presented radar images illustrate that the polarimetric characteristics of the backscattered radar signals depend on the shape and the electrophysical parameters of the RS objects. These correlations create the preconditions for the development of the highly efficient radar systems for the detection, selection, recognition and cartography on the base of the use of the invariant polarimetric signal parameters.
A new fast algorithm of 2D DWT transform is presented. The algorithm operates on byte represented images and performs image transformation with the Cohen-Daubechies-Feauveau wavelet of the second order. It uses the lifting scheme for the calculations. The proposed algorithm is based on the "checkerboard" computation scheme for non-separable 2D wavelet. The problem of data extension near the image borders is resolved computing 1D Haar wavelet in the vicinity of the borders. With the checkerboard splitting, at each level of decomposition only one detail image is produced that simplify the further analysis for data compression. The calculations are rather simple, without any floating point operation allowing the implementation of the designed algorithm in fixed point DSP processors for fast, near real time processing. The proposed algorithm does not possesses perfect restoration of the processed data because of rounding that is introduced at each level of decomposition/restoration to perform operations with byte represented data. The designed algorithm was tested on different images. The criterion to estimate quantitatively the quality of the restored images was the well known PSNR. For the visual quality estimation the error maps between original and restored images were calculated. The obtained simulation results show that the visual and quantitative quality of the restored images is degraded with number of decomposition level increasing but is sufficiently high even after 6 levels. The introduced distortion are concentrated in the vicinity of high spatial activity details and are absent in the homogeneous regions. The designed algorithm can be used for image lossy compression and in noise suppression applications.
We present the implementation of real-time image filtering with retention of small-size details by means of use of DSP TMS320C6701. The filtering scheme is given for two filters connected in cascade. The first filter uses a similar scheme to KNN filter to provide the preservation of small-size details and the redescending M-estimators combined with the median estimator to provide impulsive noise rejection. The second filter uses an M filter to provide multiplicative noise suppression. We use different types of influence functions in the M-estimator to provide complex noise suppression. The efficiency of the proposed filter has been evaluated by numerous simulations.
In this paper, we present the implementation of the robust detail preserving filters with complex noise suppression for image processing applications. The designed filter is the consequential connection of two filters. The first filter uses the value of central pixel of the filtering window to provide the preservation of fine details and the redescending M-estimators combined with the median estimator to provide impulsive noise rejection. The second filter uses the output of the first filter as the pre-estimator for an adaptive calculation in the redescending M-estimator. We investigated various types of influence functions in the M-estimator those are similar to the ones used in the Sigma filter to provide multiplicative noise suppression. The optimal values of the parameters of designed filters in presence of different noise mixture are determined. Different simulation data are presented in the paper and shown the statistical efficiency of the filters.
A novel filtering algorithm applicable to image processing is presented. It was designed using rank-ordered mean (ROM) estimator to remove an outlier and robust local data activity estimators to detect the outliers. The proposed filter effectively remove impulse noise and preserve edge and fine details. The filter possesses good visual quality of the processed simulated images and good quantitative quality in comparison to the standard median filter. Recommendations to obtain best processing results by proper selection of the filter parameters are given. The designed filter is suitable for impulse noise removal in any image processing applications. One can use it at the first stage of image enhancement followed by any detail-preserving techniques such as the Sigma filter at the second stage.
In this paper, we present the real time implementation of the robust RM-estimators with different influence functions such as the cut median, Hampel, Andrews sine, Tukey and Bernoulli functions. The use of these functions in the RM algorithms provides the retention of small-size details, impulsive noise removal and multiplicative noise suppression. They demonstrated better robustness in comparison with the use of the simplest cut function. The optimal values of the parameters of such filters in presence of different noise mixture are determined. The implementation by means of use of DSP TMS320C6701 has demonstrated that the values of time processing in the case of the simplest cut function is less in comparison with another influence functions, but noise suppression is better when the proposed functions were applied.
In this paper, we present implementation of the robust RM- estimators with different influence functions such as the cut median (skipped median) function and Hampel function. We obtained that use of these functions in the RM algorithms demonstrated better robustness in comparison with the simplest cut median function. Applications of these functions in filtering procedures provide the preservation of fine details, impulsive noise removal and suppression of the multiplicative noise. The implementation of the cut median and Hampel functions in the RM-KNN filter has shown that its use is a good tool for preservation of fine details and suppression of noise by means of use DSP TMS320C6701. The deterministic and statistical properties of the designed filters have been investigated and shown their effectiveness. The optimal values for parameters of these filters for different noise mixture are presented in this paper. Finally, DSP implementation has demonstrated that in the case of use the simplest cut median the time of processing is less than in the case of applications the cut median and Hampel functions, but nosie suppression is better when cut median or Hampel functions were applied.
Robust image filter that can provide the preservation of fine details and effective suppression of multiplicative noise is presented. This filter bases on the M (robust maximum likelihood) estimators and R (rank) estimators derived from the statistical theory of rank tests. The filter proposed consists of two stages: at the first stage, to realize the rejection of impulsive noise, we presented the image filter with an adaptive spike detector and M-estimator modified by median estimator to have the ability to remove outliers. The second stage filter, a modified sigma filter, provides the multiplicative noise suppression combined with detail preserving scheme of a Lee filter. Numerical analysis of the simulation results shows that the proposed image filter has good preservation of fine details, effective multiplicative noise suppression and impulsive noise removal for different type of images in the sense of small detail percentage.
Differential GPS (DGPS) is the system used for improving accuracy in GPS position and velocity estimation. Measurements can be obtained in real time with a high level of accuracy by means of use DGPS for task the vehicle tracking, dispatching, location, navigation, etc. In this paper we present a modified Kalman filter for DGPS to achieve an accurate estimation of the position and velocity. The proposed and realized algorithms in DGPS system can be implemented by low cost commercial C/A code GPS modules. With the help of Kalman filter the reducing of the anti-common errors between the users and reference station has been achieved. Two variants of the Kalman filters have been investigated. It is presented the experimental testing of the performance of DGPS with Kalman filtering. The used filtering procedure has shown the possibility to reduce the anti-common errors. The proposed and investigated procedures of Kalman filtering could be used for better positioning in the different navigation and positioning applications.
In this paper, we present a robust image filter that provides preservation of fine details and effective suppression of intensive multiplicative noise. The filter is based on the use of M (robust maximum likelihood)-estimators and R(rank)-estimators derived from the statistical theory of rank tests. At the first stage, to provide impulsive noise rejection, the introduced image filters uses the central pixel of the filtering window and the redescending M-estimators combined with the median estimator. At the second stage, to provide multiplicative noise suppression, a modified Sigma filter that implements the calculation scheme of a redescending M-estimator, is used. Visual and analytical analysis of simulation results shows that the proposed image filter has demonstrated fine detail preservation, good multiplicative noise suppression and impulsive noise removal.
In this paper, we present a robust image filtering algorithms that provide preservation of fine details and strong speckle nose suppression. They were derived using our approach for robust filter design. According to this approach, we used M-estimators and R-estimators derived from the statistical theory of rank tests. At the first stage, to provide impulsive noise rejection, the introduced robust image filters use the central pixel of the filtering window and the redescending M-estimators combined with the median or Wilcoxon estimators. At the second stage, to provide multiplicative noise suppression, a modified Sigma filter that implements the iterative calculation scheme of a redescending M-estimator, is used. The proposed robust rank detail-p[reserving filter demonstrated excellent fine detail preservation and impulsive noise removal. Visual and analytical analysis of these results shows the algorithm proposed on the base of RM approach provide good visual quality of processed data and possess good speckle noise attenuation capabilities.
In this paper we discuss the problem of subsurface target imaging in lossy, inhomogeneous medium with the presence of the air-ground interface. A subsurface imaging radar system using optimal processing procedures and multifrequency holographic approach and characterizing with controlling parameters has been analyzed. It was found the values of contrast coefficient for subsurface object reconstruction on the background signal scattered by upper surface layer that demonstrate high resolution of the image estimated. The resolution of reconstructed images depends on the synthetic aperture length, soil type (electric conductivity and dielectric permittivity), geometrical parameters, central frequency, frequency band and antenna directivity. The novel filtering techniques was proposed in this paper. By mean of use new filters it is possible to increase the quality of the subsurface imaging. Different results of the numerical calculation and simulation of the filtering algorithms are presented. These results show the effectiveness of new algorithms of reconstruction and filtration in the problem of subsurface radar imaging.
Novel robust filtering algorithms applicable to image processing are introduced. They were derived using robust M-type point estimators and the restriction technique of the well-known KNN filter. The derived filters effectively remove impulse noise and preserve edge and fine details. The proposed filters provide excellent visual quality of the processed simulated images and good quantitative quality in the MSE sense in comparison to the standard median filter. Recommendations to obtain best processing results by proper selection of derived filter parameters are given. Two derived filters are suitable for impulse noise reduction in any image processing applications. One can use the RM-KNN filters at the first stage of image enhancement followed by any detail-preserving techniques such as the Sigma filter at the second stage.
We introduce novel robust filtering algorithms applicable to image and signal processing in the remote sensing applications. They were derived using RM-type point estimators and the restriction technique of the well-known specific for image processing KNN filter. Novel RM-KNN filters effectively remove impulsive noise while edge and fine details are preserved. The proposed filters were tested on simulated images and radar data and were provided excellent visual quality of the processed images and good quantitative quality in the MSE sense over standard median filter. Recommendations to obtain best processing results by proper selection of derived filter parameters are given in this paper. Two derived filters are suitable for impulsive noise reduction in the remote sensing image processing applications. RM-KNN filters can be used as the first stage of image enhancement following by any non-robust techniques such as Sigma-filter on the second stage.
We introduce novel robust filtering applicable to image processing. They were derived using RM-type point estimations and the restriction technique of the well-known specific for image processing KNN filter. The derived RM-KNN filters effectively remove impulsive noise while edge and fine details are preserved. The proposed filters were tested on simulated images and real data and were provided excellent visual quality of the processed images and good quantitative quality in the MSE sense over standard median filter. Recommendations to obtain best processing result by proper selection of derived filter parameters are given. Two derived filters are suitable for impulsive noise reduction in any image processing applications. One can use the RM-KNN filters as the first stage of image enhancement following by any non-robust techniques such as Sigma-filter on the second stage.
Novel robust algorithms using combined rank and M-estimations applicable to image and signal processing are proposed. They are based on analysis of statistical characteristics of images and permit to enhance the quality of radar images by means of effective speckle suppression, impulsive noise removal and edge preservation. The advantages of these digital algorithms are demonstrated by simulated images and real data.
Novel adaptive robust filtering algorithms applicable to radar image processing are proposed. They take into consideration the peculiarities of radar images and possess a good combination of properties: effective speckle suppression, impulsive noise removal, edge and detail preservation and low computational complexity. The advantages of these digital algorithms are demonstrated by simulated
data and images obtained by airborne side-look non SAR radar.
Homomorphic filter approach for image processing is very well known as a way for image dynamic range and increasing contrast. According to this approach, input signal is assumed to consist of two multiplicative components: background and details. The standard problem in processing such signals involves logarithm operation, division on two components by implementing low frequency and high-pass filters, addition of evaluations multiplied by different gain coefficients, and exponent calculation. In this paper we propose to use median filter for deriving multiplicative component evaluations. It was found that the proposed homomorphic filter has several useful properties in remote sensing image enhancement applications. Experimental results for simulated and real image processing are presented in the paper.
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