Peng-Lang Shui

Image denoising algorithm via doubly local Wiener filtering with directional windows in wavelet domain

Local Wiener filtering in the wavelet domain is an effective image denoising method of low complexity. In this letter, we propose a doubly local Wiener filtering algorithm, where the elliptic directional windows are used for different oriented subbands in order to estimate the signal variances of noisy wavelet coefficients, and the two procedures of local Wiener filtering are performed on the noisy image. The experimental results show that the proposed algorithm improves the denoising performance significantly.

Noise-robust edge detector combining isotropic and anisotropic Gaussian kernels

A new noise-robust edge detector is proposed, which combines a small-scaled isotropic Gaussian kernel and large-scaled anisotropic Gaussian kernels (ANGKs) to obtain edge maps of images. Its main advantage is that noise reduction is attained while maintaining high edge resolution. From the ANGKs, anisotropic directional derivatives (ANDDs) are derived to capture the locally directional variation of an image. The ANDD-based edge strength map (ESM) is constructed. Its noise-robustness is determined by the scale alone and its edge resolution by the ratio of the scale to the anisotropic factor. Moreover, the edge stretch effect in anisotropic smoothing is revealed. The ANDD-based ESM and the gradient-based ESM with a small-scaled isotropic Gaussian kernel are fused into a noise-robust ESM with high edge resolution and little edge stretch. Embedding the fused ESM into the routine of Canny detector, a noise-robust edge detector is developed, which includes two additional modifications: contrast equalization and noise-dependent lower threshold. The aggregate test receiver-operating-characteristic (ROC) curves and the Pratts Figure of Merit (FOM) are used to evaluate the proposed detector by abundant experiments. The experimental results show that the proposed detector can obtain high-quality edge maps for noise-free and noisy images.

Nonparametric Detection of FM Signals Using Time-Frequency Ridge Energy

In many practical applications, signals to be detected are unknown nonlinear frequency modulated (FM) and are corrupted by strong noise. The phase histories of signals are assumed to be unknown smooth functions of time and these functions are poorly modeled or unmodeled by a small number of parameters. Thus, the conventional parametric-based detection methods are invalid in these cases. This paper proposes a nonparametric detection method using the ridge energy of observations. The detection process consists of three steps, TF ridge detection, ridge energy extraction, and decision. First, the directionally smoothed-pseudo-Wigner-Ville distribution (DSPWVD) is introduced to highlight the instantaneous frequency (IF) points along a special direction on the IF curve of a signal from noise. Further, an angular maximal distribution (AMAD) is constructed from a set of DSPWVDs to highlight the entire IF curve. As a result, the TF ridge of an observation can be estimated well from its AMAD by the maxima position detector. Second, the ridge energy, the total energy along the TF ridge on the pseudo-Wigner-Ville distribution (PWVD), is extracted. A noisy signal has larger ridge energy than a pure noise does, with a large probability, because pure noise energy is randomly distributed throughout the TF plane while the signal energy in a noisy signal is concentrated along the estimated TF ridge. Third, the ridge energy of an observation is used as the test statistic to decide whether or not a signal of interest is present in the observation, where the decision threshold is determined by a large number of Monte Carlo simulations using pure noise. Finally, the simulation experiments to two test signals are made to verify the effectiveness of the proposed method.

Corner Detection and Classification Using Anisotropic Directional Derivative Representations

This paper proposes a corner detector and classifier using anisotropic directional derivative (ANDD) representations. The ANDD representation at a pixel is a function of the oriented angle and characterizes the local directional grayscale variation around the pixel. The proposed corner detector fuses the ideas of the contour- and intensity-based detection. It consists of three cascaded blocks. First, the edge map of an image is obtained by the Canny detector and from which contours are extracted and patched. Next, the ANDD representation at each pixel on contours is calculated and normalized by its maximal magnitude. The area surrounded by the normalized ANDD representation forms a new corner measure. Finally, the nonmaximum suppression and thresholding are operated on each contour to find corners in terms of the corner measure. Moreover, a corner classifier based on the peak number of the ANDD representation is given. Experiments are made to evaluate the proposed detector and classifier. The proposed detector is competitive with the two recent state-of-the-art corner detectors, the He & Yung detector and CPDA detector, in detection capability and attains higher repeatability under affine transforms. The proposed classifier can discriminate effectively simple corners, Y-type corners, and higher order corners.

Range-Spread Target Detection Based on Cross Time-Frequency Distribution Features of Two Adjacent Received Signals

High resolution radars (HRRs) transmit a wideband signal to achieve a high range resolution. A target is considered as composed of multiple scatterers, which occupy or spread in multiple radar range cells with several scatterers in each cell. Therefore, the reflection of a target spreads in multiple range cells in the received signal, which contains more information of target than that obtained from low resolution radars. The target in high resolution radar systems is a range-spread target. The range-spreading or echo features of target are utilized for target detection and identification. The echoes of target are convolutions of transmitted signals with target range-scattering functions dependent on the gesture of target to the line of radar sight. A single echo is used in the conventional detection. It is difficult for target detection and identification in low signal-to-noise ratio (SNR) condition. In this paper, we propose a new range-spread target detection scheme exploiting the image features of cross time-frequency distribution (TFD) of a pair of adjacent received signals. After dechirping, the received signal reflected from target consists of multiple sinusoidal components due to its multiple scatterers when a linear frequency modulated (LFM) signal is transmitted from radar. Some regular image patterns or features of target appear in the cross TFD of two adjacent received signals, while the cross TFD of two independent Gaussian noises does not show such patterns. The cross TFD features are exploited in the proposed scheme. Three steps are composed in the proposed scheme. Firstly, a cross smoothed-pseudo Wigner-Ville distribution (CSPWVD) is made for two adjacent received signals to generate a two-dimensional (2-D) TF image. Then, some regular geometric patterns are detected and extracted from the image. At last, two features of the extracted geometric patterns are jointly utilized to detect target. The proposed algorithm is verified by using raw radar data. It outperforms the conventional detection methods.

Variational Models for Fusion and Denoising of Multifocus Images

In this letter, variational models in pixel domain and wavelet domain are presented for fusion and denoising of noisy multifocus images. In pixel domain, the problem is formulized as minimizing a weighted energy functional, where the total variation (TV) is used as regularity constraint for noise reduction. A new family of weight functions for fusion is proposed that are based on the local average modulus of gradients and the power transform. In wavelet domain, the problem is formulized as shrinkage of the weighted wavelet coefficients of source images, where weight functions are based on the local average modulus of intra- and inter-scale wavelet coefficients and the power transform. The experiments are made to verify the effectiveness of the proposed methods.

Edge Detector of SAR Images Using Gaussian-Gamma-Shaped Bi-Windows

By introducing Gaussian-Gamma-shaped (GGS) bi-windows instead of traditional rectangle bi-windows, a new ratio-based edge detector is proposed to extract thin edges of synthetic aperture radar (SAR) images. As poor 2-D smoothing filters, the rectangle window functions are shown to be apt to incur false-edge pixels near true edges. Using the GGS window functions reduces false-edge pixels near true edges, which can be verified by analyzing effective false maxima in the edge strength maps (ESMs). Operating the nonmaximum suppression and hysteresis thresholding on the ratio-based ESM using GGS bi-windows yields thin edges of SAR images. The receiver-operating-characteristic curve is used to evaluate edge detectors. The experimental results to a synthetic SAR image show that the detector using GGS bi-windows attains better performance than the one using rectangle bi-windows.

Image denoising algorithm using doubly local Wiener filtering with block-adaptive windows in wavelet domain

In this paper, we propose the block-adaptive windows that are used to upgrade the image denoising performance of the doubly local Wiener filtering method and the corresponding algorithm is also used to reduce spatially non-stationary additive white Gaussian noise (SNS-AWGN) in images. Based on the fact that the energy clusters in the detail subimages of an image exhibit direction features varying with spatial locations and oriented subbands, a noisy detail subimage is divided into non-overlapping small-size blocks and the spatial energy correlation function of each block is calculated to determine the principal direction of energy clusters within each block and the corresponding block-adaptive window. The block-adaptive windows are used to improve the estimations of the images energy distribution in the detail subimages. For noisy images corrupted by SNS-AWGNs, non-uniform noise variances in the pixel domain must be estimated. To do that, we propose the joint neighborhood median absolute deviation (JNMAD) estimator, which makes the denoising algorithm able to be used in the cases of SNS-AWGNs. The experimental results show that the doubly local Wiener filtering method with block-adaptive windows is superior to other wavelet-based methods using two-dimensional separable wavelet transforms in the case of stationary noise and provides satisfactory performance in the cases of SNS-AWGNs.

M-band compactly supported orthogonal symmetric interpolating scaling functions

In many applications, wavelets are usually expected to have the following properties: compact support, orthogonality, linear-phase, regularity, and interpolation. To construct such wavelets, it is crucial designing scaling functions with the above properties. In two- and three-band cases, except for the Haar functions, there exists no scaling function with the above five properties. In M-band case (M/spl ges/4), more free degrees available in design enable us to construct such scaling functions. A novel approach to designing such scaling functions is proposed. First, we extend the two-band Dubuc (1986) filters to the M-band case. Next, the M-band FIR regular symmetric interpolating scaling filters are parameterized, and then, M-band FIR regular orthogonal symmetric interpolating scaling filters (OSISFs) are designed via optimal selection of parameters. Finally, two family of four-band and five-band OSISFs and scaling functions are developed, and their smoothness are estimated.

Range-Spread Target Detection using Consecutive HRRPs

In this paper, a heuristic detector is proposed to detect range-spread targets in white Gaussian noise using multiple consecutive high- resolution range profiles (HRRPs) received from a high-resolution radar (HRR). The detector consists of refiners of HRRPs and a cross-correlation integrator of refined HRRPs. Based on the fact that strong scattering cells are sparse in target HRRPs, nonlinear shrinkage maps are designed to refine received HRRPs before integration, by which most of the noise-only cells in received HRRPs are suppressed while strong scattering cells most probably relevant to target signature are preserved. Since the targets scattering geometry is almost unchanged except for range walking during integration, the refined target HRRPs from consecutive pulses are highly similar while refined noise-only HRRPs are dissimilar due to randomicity. The modified correlation matrix of multiple refined HRRPs is used to measure their similarity. The test statistic, a weighted integration of the entries of the modified correlation matrix, is constructed for target detection. The proposed detector does not depend on a strict target return model and can work in mild conditions. The real target data and simulated noise are used to evaluate the detector, and the experimental results show that it achieves better detection performance than some existing methods.