离焦模糊图像的自适应滤波及逆滤波器复原

在微操作中,显微视觉系统获取的图像通常是离焦模糊图像.根据最小二乘原理和回归模型设计自适应滤波器,用于消除图像噪声,提高图像的信噪比;离焦模糊图像的退化模型可用圆盘函数描述,利用模糊图像频域的零点位置来估计圆盘函数的模糊参数;采用基于简化Wiener滤波的逆滤波器方法对模糊图像进行复原.对算法进行了仿真和实验分析,结果表明,该方法能够以较少的运算时间代价获取较好的复原效果.     

Hybrid order statistic filter and its application to image restoration

We introduce a new nonlinear filter for signal and image restoration, the hybrid order statistic (HOS) filter. Because it exploits both rank- and spatial-order information, the HOS realizes the advantages of nonlinear filters in edge preservation and reduction of impulsive noise components while retaining the ability of the linear filter to suppress Gaussian noise. We show that the HOS filter exhibits improved performance over both the linear Wiener and the nonlinear L filters in reducing mean-squared error in the presence of contaminated Gaussian noise. In many cases it also performs favorably compared with the Ll and rank-conditioned rank selection filters.     

Noniterative blind data restoration by use of an extracted filter function

A signal-processing algorithm has been developed where a filter function is extracted from degraded data through mathematical operations. The filter function can then be used to restore much of the degraded content of the data through use of a deconvolution algorithm. This process can be performed without prior knowledge of the detection system, a technique known as blind deconvolution. The extraction process, designated self-deconvolving data reconstruction algorithm, has been used successfully to restore digitized photographs, digitized acoustic waveforms, and other forms of data. The process is noniterative, computationally efficient, and requires little user input. Implementation is straightforward, allowing inclusion into many types of signal-processing software and hardware. The novelty of the invention is the application of a power law and smoothing function to the degraded data in frequency space. Two methods for determining the value of the power law are discussed. The first method assumes the power law is frequency dependent. The function derived comparing the frequency spectrum of the degraded data with the spectrum of a signal with the desired frequency response. The second method assumes this function is a constant of frequency. This approach requires little knowledge of the original data or the degradation.     

Constrained digital matched filter method for optimum filter synthesis

We present a new method to directly calculate the optimum filter in presence of any additive stationary noise, with arbitrary time and domain constraints (flat-top, zero-area, etc.). A more concise re-deduction of digital penalized LMS method (DPLMS) is given. This method is fully developed, and synthesis results of a typical situation are given and compared with the DPLMS method. Optimum filter can be synthesized without a prior knowledge of the noise power spectral density, which makes it suitable to be used in adaptive, self-calibrating digital spectroscopy.     

Classifier-augmented median filters for image restoration

Developed in this paper is a new approach that augments a fuzzy classifier to determine whether or not the operating pixel, centered in the sliding window, should be involved with the impulse noise filtering process. Owing to the inclusion of the fuzzy K-nearest neighbor (K-NN) scheme, any central operating pixel that is not noise corrupted can be effectively detected and then left unchanged. Thus, the unnecessary pixel replacement can be avoided and the details and signal structure of the image will be best retained. If the center point is found to be noise corrupted, the proposed classifier-augmented median filter facilitates the filtering action only on a subset of pixels, which are not noise contaminated in the window. Due to this impulse pixel exclusion, the biased estimation caused from impulses can be eliminated and, thus, obtains a better estimation of the center pixel. Experimental results showed that this new approach largely outperformed several existing schemes for image noise removal.     

Distortion-invariant filter for nonoverlapping noise

A new heuristic filter based on the optimum filter for disjoint noise developed by Javidi and Wang [J. Opt. Soc. Am. A 11, 2604 (1995)] is presented. In this new filter a number of optimum filters built from single training images are combined linearly by use of the synthetic discriminant function (SDF) approach into a distortion-invariant filter for disjoint noise. Like the traditional SDF approach, this summation technique makes it possible to control the height of the correlation peak easily, for example, if a uniform filter response is needed. The filter is compared with the distortion-invariant version of the optimum filter on images with low contrast and high levels of nonoverlapping clutter. The new filter shows good results, demonstrating that it is, with very simple heuristic methods, possible to improve the performance of distortion-invariant filters for nonoverlapping noise.     

Signal-processing approaches for image-resolution restoration for TOMBO imagery

Thin observation module by bounded optics (TOMBO) is an optical system that achieves compactness and thinness by replacing a conventional large full aperture by a lenslet array with several smaller apertures. This array allows us to collect diverse low-resolution measurements. Finding an efficient way of combining these diverse measurements to make a high-resolution image is an important research problem. We focus on finding a computational method for performing the resolution restoration and evaluating the method via simulations. Our approach is based on advanced signal-processing concepts: we construct a computational data model based on Fourier optics and propose restoration algorithms based on minimization of an information-theoretic measure, called Csiszár's I divergence between two nonnegative quantities: the measured data and the hypothetical images that are induced by our algorithms through the use of our computational data model. We also incorporate Poisson and Gaussian noise processes to model the physical measurements. To solve the optimization problem, we adapt the popular expectation-maximization method. These iterative algorithms, in a multiplicative form, preserve powerful nonnegativity constraints. We further incorporate a regularization based on minimization of total variation to suppress incurring artifacts such as roughness on the surfaces of the estimates. Two sets of simulation examples show that the algorithms can produce very high-quality estimates from noiseless measurements and reasonably good estimates from noisy measurements, even when the measurements are incomplete. Several interesting and useful avenues for future work such as the effects of measurement selection are suggested in our conclusional remarks.     

Depth-variant maximum-likelihood restoration for three-dimensional fluorescence microscopy

We derive an algorithm for maximum-likelihood image estimation on the basis of the expectation-maximization (EM) formalism by using a new approximate model for depth-varying image formation for optical sectioning microscopy. This new strata-based model incorporates spherical aberration that worsens as the microscope is focused deeper under the cover slip and is the result of the refractive-index mismatch between the immersion medium and the mounting medium of the specimen. Images of a specimen with known geometry and refractive index show that the model captures the main features of the image. We analyze the performance of the depth-variant EM algorithm with simulations, which show that the algorithm can compensate for image degradation changing with depth.     

Sparsity constrained regularization for multiframe image restoration

In this paper we present a new algorithm for restoring an object from multiple undersampled low-resolution (LR) images that are degraded by optical blur and additive white Gaussian noise. We formulate the multiframe superresolution problem as maximum a posteriori estimation. The prior knowledge that the object is sparse in some domain is incorporated in two ways: first we use the popular l(1) norm as the regularization operator. Second, we model wavelet coefficients of natural objects using generalized Gaussian densities. The model parameters are learned from a set of training objects, and the regularization operator is derived from these parameters. We compare the results from our algorithms with an expectation-maximization (EM) algorithm for l(1) norm minimization and also with the linear minimum-mean-squared error (LMMSE) estimator. Using only eight 4 x 4 pixel downsampled LR images the reconstruction errors of object estimates obtained from our algorithm are 5.5% smaller than by the EM method and 14.3% smaller than by the LMMSE method.     

A restoration framework for ultrasonic tissue characterization

Ultrasonic tissue characterization has become an area of intensive research. This procedure generally relies on the analysis of the unprocessed echo signal. Because the ultrasound echo is degraded by the non-ideal system point spread function, a deconvolution step could be employed to provide an estimate of the tissue response that could then be exploited for a more accurate characterization. In medical ultrasound, deconvolution is commonly used to increase diagnostic reliability of ultrasound images by improving their contrast and resolution. Most successful algorithms address deconvolution in a maximum a posteriori estimation framework; this typically leads to the solution of l(2)-norm or (1)-norm constrained optimization problems, depending on the choice of the prior distribution. Although these techniques are sufficient to obtain relevant image visual quality improvements, the obtained reflectivity estimates are, however, not appropriate for classification purposes. In this context, we introduce in this paper a maximum a posteriori deconvolution framework expressly derived to improve tissue characterization. The algorithm overcomes limitations associated with standard techniques by using a nonstandard prior model for the tissue response. We present an evaluation of the algorithm performance using both computer simulations and tissue-mimicking phantoms. These studies reveal increased accuracy in the characterization of media with different properties. A comparison with state-of-the-art Wiener and l(1)-norm deconvolution techniques attests to the superiority of the proposed algorithm.     

Communication theoretic image restoration for binary-valued imagery

We present a new image-restoration algorithm for binary-valued imagery. A trellis-based search method is described that exploits the finite alphabet of the target imagery. This algorithm seeks the maximum-likelihood solution to the image-restoration problem and is motivated by the Viterbi algorithm for traditional binary data detection in the presence of intersymbol interference and noise. We describe a blockwise method to restore two-dimensional imagery on a row-by-row basis and in which a priori knowledge of image pixel correlation structure can be included through a modification to the trellis transition probabilities. The performance of the new Viterbi-based algorithm is shown to be superior to Wiener filtering in terms of both bit error rate and visual quality. Algorithmic choices related to trellis state configuration, complexity reduction, and transition probability selection are investigated, and various trade-offs are discussed.     

Relaxed ordered-subset algorithm for penalized-likelihood image restoration

The expectation-maximization (EM) algorithm for maximum-likelihood image recovery is guaranteed to converge, but it converges slowly. Its ordered-subset version (OS-EM) is used widely in tomographic image reconstruction because of its order-of-magnitude acceleration compared with the EM algorithm, but it does not guarantee convergence. Recently the ordered-subset, separable-paraboloidal-surrogate (OS-SPS) algorithm with relaxation has been shown to converge to the optimal point while providing fast convergence. We adapt the relaxed OS-SPS algorithm to the problem of image restoration. Because data acquisition in image restoration is different from that in tomography, we employ a different strategy for choosing subsets, using pixel locations rather than projection angles. Simulation results show that the relaxed OS-SPS algorithm can provide an order-of-magnitude acceleration over the EM algorithm for image restoration. This new algorithm now provides the speed and guaranteed convergence necessary for efficient image restoration.     

Power spectrum equalization for ultrasonic image restoration

A method of image restoration for ultrasonic B-scan images has been proposed that need no a priori knowledge on the PSF (point spread function) of the imaging system and is feasible for in vivo applications. The entire system's response, including the interposed medium and possible transducer defects, is estimated from the degraded image itself with a few simple operations. The ultrasonic image is restored based only on a knowledge of the estimated PSF and on the spectral characteristics of the resultant echo signal. The proposed method does not modify the phase relations between echoes from multiple scatterers since the restoration filter is phaseless and the display operation does not involve nonlinear detection. The effectiveness of the restoration filter was tested on simulated ultrasonic images in the absence and in the presence of interposed tissue. Then the filter was tested on a phantom made of scatterers randomly distributed in nonattenuating gel with and without an interposed medium whose attenuation linearly increases with frequency. A good correspondence between simulations and experimental results was found: both tests show an exceptional improvement of image resolution.