Restoration of ultrasonic NDE images

The problem of determining the ultrasonic reflectivity function resulting from density changes in a material is posed as an image restoration problem. The received time-domain signal is modeled as resulting from convolution of the acoustic pulse with the reflectivity function with additive noise. A new deconvolution method is used to estimate the reflectivity function. The method operates by an iterative maximum-a-posteriori (MAP) optimization method that not only estimates the deconvolved function but incorporates the a prior knowledge that the material is relatively uniform, except for step discontinuities. Finally, an annealing algorithm is added that allows the optimization to avoid many local minima. Initial results, based on application of the method to synthetic signals and to real signals from a nondestructive evaluation sensor, are presented     

Restoration of lossy compressed noisy images

Noise degrades the performance of any image compression algorithm. However, at very low bit rates, image coders effectively filter noise that may he present in the image, thus, enabling the coder to operate closer to the noise free case. Unfortunately, at these low bit rates the quality of the compressed image is reduced and very distinctive coding artifacts occur. This paper proposes a combined restoration of the compressed image from both the artifacts introduced by the coder along with the additive noise. The proposed approach is applied to images corrupted by data-dependent Poisson noise and to images corrupted by film-grain noise when compressed using a block transform-coder such as JPEG. This approach has proved to be effective in terms of visual quality and peak signal-to-noise ratio (PSNR) when tested on simulated and real images.     

Restoration and reconstruction of AVHRR images

Describes the design of small convolution kernels for the restoration and reconstruction of Advanced Very High Resolution Radiometer (AVHRR) images. The kernels are small enough to be implemented efficiently by convolution, yet effectively correct degradations and increase apparent resolution. The kernel derivation is based on a comprehensive, end-to-end system model that accounts for scene statistics, image acquisition blur, sampling effects, sensor noise, and postfilter reconstruction. The design maximizes image fidelity subject to explicit constraints on the spatial support and resolution of the kernel. The kernels can be designed with finer resolution than the image to perform partial reconstruction for geometric correction and other remapping operations. Experiments demonstrate that small kernels yield fidelity comparable to optimal unconstrained filters with less computation     

Restoration of randomly blurred images via the maximum a posterioricriterion

The maximum a posteriori (MAP) estimation technique is applied to the problem of restoring images distorted by noisy point spread functions and additive noise. The resulting MAP estimator is nonlinear and is obtained by numerically maximizing a conditional probability density function. The energy nonnegativity constraint is incorporated in the optimization process. Although the deblurring results are slightly inferior to those obtained by applying the Wiener criterion, the advantage of the MAP estimator lies in its significant suppression of noise     

Resolution limits in signal recovery

Resolution analysis for the problem of signal recovery from finitely many linear measurements is the subject of this paper. The classical Rayleigh limit serves only as a lower bound on resolution since it does not assume any recovery strategy and is based only on observed data. We show that details finer than the Rayleigh limit can be recovered by simple linear processing that incorporates prior information. We first define a measure of resolution based on allowable levels of error that is more appropriate for current signal recovery strategies than the Rayleigh definition. In the practical situation in which only finitely many noisy observations are available, we have to restrict the class of signals in order to make the resolution measure meaningful. We consider the set of bandlimited and essentially timelimited signals since it describes most signals encountered in practice. For this set, we show how to precompute resolution limits from knowledge of measurement functionals, signal-to-noise ratio, passband, energy concentration regions, energy concentration factor, and a prescribed level of error tolerance. In the process, we also derive an algorithm for high-resolution signal recovery. We illustrate the results with examples in one and two dimensions     

Restoration of retinal images obtained through cataracts

An optical model for imaging the retina through cataracts has been developed. The images are treated as sample functions of stochastic processes. On the basis of the model a homomorphic Weiner filter can be designed that will optimally restore the cataractous image (in the mean-square-error sense). The design of the filter requires a priori knowledge of the statistics of either the cataract transmittance function or the noncataractous image. The cataract transmittance function, assumed to be low pass in nature, can be estimated from the cataractous image of the retina. The statistics of the noncataractous image can be estimated using an old, precataractous photograph of the same retina, which is frequently available. Various modes of this restoration concept were applied to clinical photographs and found to be effective. The best results were obtained with short-space enhancement using averaged short-space estimates of the spectra of the two images.     

多尺度SVR的SAR图像复原

针对合成孔径雷达图像中存在斑点噪声的缺陷,将支持向量拟合方法引入小波系数收缩策略中,提出了基于多尺度SVR(support vector regression)的SAR图像复原算法。该方法在不同尺度下选用不同的核参数。为保护边缘结构信息,首先对各小波高频子带进行SVM拟合,然后计算原始小波系数与拟合估计值的绝对差,并定义小波系数收缩准则,根据准则对小波系数进行收缩,使复原的图像能较好的保持原有图像的纹理和结构信息。实验采用真实的SAR图像,实验结果显示该方法优于常规小波滤波和Lee滤波方法。     

Restoration of gamma camera-based nuclear medicine images

Experiments were conducted using a Siemens Rota camera to study the applicability of two linear shift-invariant (LSI) filters, namely, the Wiener and power spectrum equalization filters, for restoration of planar projections and single-photon-emission computed tomography (SPECT) images. In the restoration scheme, the system transfer function, computed from a line source image, is modeled by a 2-D Gaussian function. The noise power spectrum is modeled as a constant for planar images and as a ramp for SPECT images. The filters have been applied to restore computer-simulated 1-D and 2-D projections and SPECT images of two simple phantoms, 2-D projections of two phantoms obtained from the Siemens Rota camera, and SPECT images of a cardiac phantom obtained from the Siemens Rota camera. The filters are shown to perform partial restoration. Considerable noise suppression and detail enhancement have been observed in the restored images. quantitative measurements such as root-mean-squared error and contrast ratio have been used for objective analysis of the results, which are encouraging.     

NMR signal enhancement via a new time-frequency transform

In this paper, a reliable method to reduce the noise from nuclear magnetic resonance (NMR) signals using a recently developed linear critically sampled time-frequency transform is proposed. In addition to its low computational requirements, this transform has many theoretical advantages that make it a good candidate for NMR signal enhancement. NMR signals in the transform domain are concentrated in a few coefficients while the noise is well distributed. Performing a thresholding technique in the transform domain, therefore, significantly enhances the signal. A comparison with other signal enhancement techniques shows that this technique has a superior performance, thus confirming the theoretical expectations.     

Joint modeling and reconstruction of a compressively-sensed set of correlated images

Employing correlation among images for improved reconstruction in compressive sensing is a conceptually attractive idea, although developing efficient modeling strategies and reconstruction algorithms are often the key to achieve any potential benefit. This paper presents a novel modeling strategy and an efficient reconstruction algorithm for processing a set of correlated images, jointly taking into consideration inter-image correlation, intra-image correlation and inter-channel correlation. The approach starts with joint modeling of the entire image set in the gradient domain, which supports simultaneous representation of local smoothness, nonlocal self-similarity of every single image, and inter-image correlation. Then an efficient algorithm is proposed to solve the joint formulation, using a Split-Bregman-based technique. Furthermore, to support color image reconstruction, the proposed algorithm is extended by using the concept of group sparsity to explore inter-channel correlation. The effectiveness of the proposed approach is demonstrated with extensive experiments on both grayscale and color image sets. Results are also compared with recently proposed compressive sensing recovery algorithms.     

Dynamic nonoptogalvanic signal polarity and magnitude in prebreakdown gas discharges

Nitrogen laser pulse irradiation of prebreakdown discharges in Ne and Ar result in pulse responses strikingly similar to those reported for dynamic optogalvanic signals. For the latter, response polarity depends primarily upon atomic transition. Here, it depends primarily upon bias. Nevertheless, analysis of the results points to similar internal processes within the gas concerning metastable generation and destruction. Photoionization-assisted electron heating is an additional photon process relevant here which changes relative populations of excited states and plays a key role in reversing prebreakdown signal response polarity. It can also explain the mechanism of reported electron temperature increase in optogalvanic experiments which cannot be explained on the basis of atomic transitions.     

Unconstrained inversion of waveheight spectra from SAR images

A procedure for inverting the nonlinear relationship between the waveheight spectrum and the SAR image spectrum is presented, and this procedure is evaluated using simulated data as well as actual ERS SAR data collected near Duck, NC. Results of this nonlinear inversion are compared with those obtained from a quasi-linear estimation procedure using simulated data, in order to illustrate the effects of nonlinearities in the imaging process. These effects include the well-known azimuth falloff effect as well as the generation of harmonics which appear in the background region of the spectrum. The nonlinear inversion technique is able to reproduce the input image spectrum to high accuracy, although the wave spectrum obtained by this procedure is not necessarily the same as the input wave spectrum. In general, the estimated wave spectrum is quite similar to the portion of the input wave spectrum within the SAR passband region, but none of the energy outside the passband is recovered. The background signals due to nonlinear effects can cause large errors in the quasi-linear estimation procedure because these signals appear in regions of the spectrum where the SAR modulation transfer function (mtf) is small. Results using actual SAR data also indicate that energy within the passband is recovered fairly accurately, although energy outside the passband is clearly lost     

Restoration of polarimetric SAR images using simulated annealing

Filtering synthetic aperture radar (SAR) images ideally results in better estimates of the parameters characterizing the distributed targets in the images while preserving the structures of the nondistributed targets. However, these objectives are normally conflicting, often leading to a filtering approach favoring one of the objectives. An algorithm for estimating the radar cross-section (RCS) for intensity SAR images has previously been proposed in the literature based on Markov random fields and the stochastic optimization method simulated annealing. A new version of the algorithm is presented applicable to multilook polarimetric SAR images, resulting in an estimate of the mean covariance matrix rather than the RCS. Small windows are applied in the filtering, and due to the iterative nature of the approach, reasonable estimates of the polarimetric quantities characterizing the distributed targets are obtained while at the same time preserving most of the structures in the image. The algorithm is evaluated using multilook polarimetric L-band data from the Danish airborne EMISAR system, and the impact of the algorithm on the unsupervised H-α classification is demonstrated     

Restoration of error-diffused images using projection onto convexsets

A novel inverse halftoning method is proposed to restore a continuous tone image from a given half-tone image. A set theoretic formulation is used where three sets are defined using the prior information about the problem. A new space-domain projection is introduced assuming the halftoning is performed using error diffusion, and the error diffusion filter kernel is known. The space-domain, frequency-domain, and space-scale domain projections are used alternately to obtain a feasible solution for the inverse halftoning problem which does not have a unique solution.     

Restoration of color images by multichannel Kalman filtering

A Kalman filter for optimal restoration of multichannel images is presented. This filter is derived using a multichannel semicausal image model that includes between-channel degradation. Both stationary and nonstationary image models are developed. This filter is implemented in the Fourier domain and computation is reduced from O(Λ ³N³M⁴) to O(Λ³N³M² ) for an M×M N-channel image with degradation length Λ. Color (red, green, and blue (RGB)) images are used as examples of multichannel images, and restoration in the RGB and YIQ domains is investigated. Simulations are presented in which the effectiveness of this filter is tested for different types of degradation and different image model estimates     

Stochastic iterative algorithms for signal set design for Gaussianchannels and optimality of the L2 signal set

We propose a stochastic iteration approach to signal set design. Four practical stochastic iterative algorithms are proposed with respect to equal and average energy constraints and sequential and batch modes. By simulation, a new optimal signal set, the L2 signal set (consisting of a regular simplex set of three signals and some zero signals), is found under the strong simplex conjecture (SSC) condition (equal a priori probability and average energy constraint) at low signal-to-noise ratios (SNR). The optimality of the L1 signal set, the confirmation of the weak simplex conjecture, and two of Dunbridge's (1967) theorems are some of the results obtained by simulations. The influence of SNR and a priori probabilities on the signal sets is also investigated via simulation. As an application to practical communication system design, the signal sets of eight two-dimensional (2-D) signals are studied by simulation under the SSC condition. Two signal sets better than 8-PSK are found. Optimal properties of the L2 signal set are analyzed under the SSC condition at low SNRs. The L2 signal set is proved to be uniquely optimal in 2-D space. The class of signal sets S(M, K) (consisting of a regular simplex set of K signals and M-K zero signals) is analyzed. It is shown that any of the signal sets S(M, K) for 3⩽K⩽M-1 disproves the strong simplex conjecture for M⩾4, and if M⩾7, S(M, 2) (the L1 signal set) also disproves the strong simplex conjecture. It is proved that the L2 signal set is the unique optimal signal set in the class of signal sets S(M, K) for all M⩾4. Several results obtained by Steiner (see ibid., vol.40, no.5, p.721-31, 1994) for all M⩾7 are extended to all M⩾4. Finally, we show that for M⩾7, there exists an integer K'相似文献   

辐射目标被动探测的阵列信号反演算法研究

综合孔径微波辐射计的空间分辨率受阵列物理孔径限制,阵列波束范围内的目标无法分辨,限制了其进一步发展和应用. 阵列信号处理中的现代空间谱估计算法可以突破空域傅里叶变换限,实现波束范围内目标角度和功率的超分辨估计.文中将阵列信号处理算法引入到辐射目标探测中,建立了基于阵列信号处理的目标辐射亮温反演模型.仿真及实验结果证明该模型算法具有超越阵列波束的辐射目标超分辨估计特性,验证了模型的可靠性,并探讨了信号相关性、信噪比等对算法的影响.     

楔环贫点阵探测器图像的形状恢复方法

新型楔环贫点阵光电探测器有利于微小型视觉系统的实现，然而它采集的图像分辨率极低，不能反映与目标外在形状相同或相近的特征。为了使该探测器及其图像能够应用于目标识别，提出一种独特的形状恢复方法。以楔环中心为原点旋转探测器，获取一系列原始低分辨率图像，通过最小二乘线性插值对目标形状进行初步恢复，利用嵌入边缘指示函数的两级水平集演化算法去除图像噪声，可得到最终的高质量的目标形状。实验表明，文中提出的形状恢复方法行之有效，形状恢复率达95%。     

Adaptive recovery of a chirped signal using the RLS algorithm

This paper studies the performance of the recursive least squares (RLS) algorithm in the presence of a general chirped signal and additive white noise. The chirped signal, which is a moving average (MA) signal deterministically shifted in frequency at rate ψ, can be used to model a frequency shift in a received signal. General expressions for the optimum Wiener-Hopf coefficients, one-step recovery and estimation errors, noise and lag misadjustments, and the optimum adaptation constant (β_opt) are found in terms of the parameters of the stationary MA signal. The output misadjustment is shown to be composed of a noise (ξ₀Mβ/2) and lag term (κ/(β²ψ²)), and the optimum adaptation constant is proportional to the chirp rate as ψ^2/3 . The special case of a chirped first-order autoregressive (AR1) process with correlation (α) is used to illustrate the effect the bandwidth (1/α) of the chirped signal on the adaptation parameters. It is shown that unlike for the chirped tone, where the β_opt increases with the filter length (M), the adaptation constant reaches a maximum for M near the inverse of the signal correlation (1/α). Furthermore, there is an optimum filter length for tracking the chirped signal and this length is less than (1/α)     

Restoration of differential images for enhancement of compressed video

A modified regularization algorithm is proposed to enhance compressed video by restoring predictive-coded pictures. Since most video coding standards adopt a hybrid structure of macroblock-based motion compensation and block discrete cosine transform, the blocking artifacts occurs at both the block boundary and block interior, and the degradation process due to quantization is generated on differential images. Based on this observation, a new degradation model of differential images is presented first. Then the corresponding restoration algorithm directly processes the differential images before reconstructing decoded images. Two constraints, such as directional continuities on the block boundary and on the block interior, have been used for defining convex sets for restoring differential images. The proposed differential domain restoration algorithm is compared with the corresponding reconstructed domain algorithm using the same degradation model and equivalent set of constraints. The proposed algorithm outperforms the reconstructed domain algorithm in both analytic and experimental senses.