Robust adaptive estimator for filtering noise in images

Provides three new methods for storing images corrupted by additive noise. One is the adaptive mean median filter for preserving the details of images when restored from additive Gaussian noise. Another is the minimum-maximum method for moving outlier noise. The third method, the robust adaptive mean p-median filter, is based on a combination of the previous two methods. In the past, proposed restoration methods have generally proven to be inadequate for both detail preservation and noise suppression, but the new adaptive mean p-median filter is shown to be good at both of these tasks, while the robust adaptive mean p-median filter can give good performance even in the presence of outliers. Degraded images are processed by the proposed algorithms, with the results compared with a selection of other median-based algorithms that have been proposed in the literature.     

Optimal filtering of digital binary images corrupted byunion/intersection noise

We model digital binary image data as realizations of a uniformly bounded discrete random set (or discrete random set, for short), which is a mathematical object that can be directly defined on a finite lattice. We consider the problem of estimating realizations of discrete random sets distorted by a degradation process that can be described by a union/intersection noise model. Two distinct optimal filtering approaches are pursued. The first involves a class of "mask" filters, which arises quite naturally from the set-theoretic analysis of optimal filters. The second approach involves a class of morphological filters. We prove that under i.i.d noise morphological openings, closings, unions of openings, and intersections of closings can be viewed as MAP estimators of morphologically smooth signals. Then, we show that by using an appropriate (under a given degradation model) expansion of the optimal filter, we can obtain universal characterizations of optimality that do not rely on strong assumptions regarding the spatial interaction of geometrical primitives of the signal and the noise. The results generalize to gray-level images in a fairly straightforward manner.     

Local noise weighted filtering for emphysema scoring of low-dose CT images

Computed tomography (CT) has become the new reference standard for quantification of emphysema. The most popular measure of emphysema derived from CT is the pixel index (PI), which expresses the fraction of the lung volume with abnormally low intensity values. As PI is calculated from a single, fixed threshold on intensity, this measure is strongly influenced by noise. This effect shows up clearly when comparing the PI score of a high-dose scan to the PI score of a low-dose (i.e., noisy) scan of the same subject. In this paper, the noise variance (NOVA) filter is presented: a general framework for (iterative) nonlinear filtering, which uses an estimate of the spatially dependent noise variance in an image. The NOVA filter iteratively estimates the local image noise and filters the image. For the specific purpose of emphysema quantification of low-dose CT images, a dedicated, noniterative NOVA filter is constructed by using prior knowledge of the data to obtain a good estimate of the spatially dependent noise in an image. The performance of the NOVA filter is assessed by comparing characteristics of pairs of high-dose and low-dose scans. The compared characteristics are the PI scores for different thresholds and the size distributions of emphysema bullae. After filtering, the PI scores of high-dose and low-dose images agree to within 2%-3% points. The reproducibility of the high-dose bullae size distribution is also strongly improved. NOVA filtering of a CT image of typically 400 x 512 x 512 voxels takes only a couple of minutes which makes it suitable for routine use in clinical practice.     

Partition-based vector filtering technique for suppression of noise in digital color images.

A partition-based adaptive vector filter is proposed for the restoration of corrupted digital color images. The novelty of the filter lies in its unique three-stage adaptive estimation. The local image structure is first estimated by a series of center-weighted reference filters. Then the distances between the observed central pixel and estimated references are utilized to classify the local inputs into one of preset structure partition cells. Finally, a weighted filtering operation, indexed by the partition cell, is applied to the estimated references in order to restore the central pixel value. The weighted filtering operation is optimized off-line for each partition cell to achieve the best tradeoff between noise suppression and structure preservation. Recursive filtering operation and recursive weight training are also investigated to further boost the restoration performance. The proposed filter has demonstrated satisfactory results in suppressing many distinct types of noise in natural color images. Noticeable performance gains are demonstrated over other prior-art methods in terms of standard objective measurements, the visual image quality and the computational complexity.     

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     

Studying the influence of search rule and context shape in filtering impulse noise images with Markov chains

The paper presents an improved context-based denoising method for gray scale images affected by impulse noise. The proposed algorithm is using Markov chains to replace the detected noise with the intensity having the highest number of occurrences in similar contexts. The context of a noisy pixel consists in its neighbor pixels and is searched in a larger but limited surrounding area. We have analyzed different search methods and different context shapes. The experimental results obtained on the test images have shown that the most efficient model applies the search in form of   “*”  of contexts in form of   “+”. Besides the better denoising performance obtained on all the noise levels, the computational time has been also significantly improved with respect to our previous context-based filter which applied full search of full context. We have also compared this improved Markov filter with other denoising techniques existing in the literature, most of them being significantly outperformed.     

Motion-compensated filtering of time-varying images

This paper presents the theory of motion-compensated spatiotemporal filtering of time-varying imagery. The properties of motion trajectories and their relation to displacement fields and velocity fields are presented. The constraints that image motion places on the time-varying image in both the spatiotemporal domain and in the frequency domain are described, along with the implications of these results on motion-compensated filtering and on sampling. An iterative method for estimating motion which generalizes many pixel-oriented and block-oriented methods is presented. Motion-compensated filtering is then applied to the problems of prediction, interpolation, and smoothing.Invited Paper     

Tree coding of bilevel images

Presently, sequential tree coders are the best general purpose bilevel image coders and the best coders of halftoned images. The current ISO standard, Joint Bilevel Image Experts Group (JBIG), is a good example. A sequential tree coder encodes the data by feeding estimates of conditional probabilities to an arithmetic coder. The conditional probabilities are estimated from co-occurrence statistics of past pixels, the statistics are stored in a tree. By organizing the code length calculations properly, a vast number of possible models (trees) reflecting different pixel orderings can be investigated within reasonable time prior to generating the code. A number of general-purpose coders are constructed according to this principle. Rissanen's (1989) one-pass algorithm, context, is presented in two modified versions. The baseline is proven to be a universal coder. The faster version, which is one order of magnitude slower than JBIG, obtains excellent and highly robust compression performance. A multipass free tree coding scheme produces superior compression results for all test images. A multipass free template coding scheme produces significantly better results than JBIG for difficult images such as halftones. By utilizing randomized subsampling in the template selection, the speed becomes acceptable for practical image coding     

Scalable coding of encrypted images

This paper proposes a novel scheme of scalable coding for encrypted images. In the encryption phase, the original pixel values are masked by a modulo-256 addition with pseudorandom numbers that are derived from a secret key. After decomposing the encrypted data into a downsampled subimage and several data sets with a multiple-resolution construction, an encoder quantizes the subimage and the Hadamard coefficients of each data set to reduce the data amount. Then, the data of quantized subimage and coefficients are regarded as a set of bitstreams. At the receiver side, while a subimage is decrypted to provide the rough information of the original content, the quantized coefficients can be used to reconstruct the detailed content with an iteratively updating procedure. Because of the hierarchical coding mechanism, the principal original content with higher resolution can be reconstructed when more bitstreams are received.     

Adaptive robust impulse noise filtering

It is well known that when data is contaminated by non-Gaussian noise, conventional linear systems may perform poorly. The paper presents an adaptive robust filter (adaptive preprocessor) for canceling impulsive components when the nominal process (or background noise) is a correlated, possibly nonstationary, Gaussian process. The proposed preprocessor does not require iterative and/or batch processing or prior knowledge about the nominal Gaussian process; consequently, it can be implemented in real time and adapt to changes in the environment. Based on simulation results, the proposed adaptive preprocessor shows superior performances over presently available techniques for cleaning impulse noise. Using the proposed adaptive preprocessor to clean the impulsive components in received data samples, conventional linear systems based on the Gaussian assumption can work in an impulsive environment with little if any modification. The technique is applicable to a wide range of problems, such as detection, power spectral estimation, and jamming or clutter suppression in impulsive environments     

Compression of multispectral images by spectral classification andtransform coding

This paper presents a new technique for the compression of multispectral images, which relies on the segmentation of the image into regions of approximately homogeneous land cover. The rationale behind this approach is that, within regions of the same land cover, the pixels have stationary statistics and are characterized by mostly linear dependency, contrary to what usually happens for unsegmented images. Therefore, by applying conventional transform coding techniques to homogeneous groups of pixels, the proposed algorithm is able to effectively exploit the statistical redundancy of the image, thereby improving the rate distortion performance. The proposed coding strategy consists of three main steps. First, each pixel is classified by vector quantizing its spectral response vector, so that both a reliable classification and a minimum distortion encoding of each vector are obtained. Then, the classification map is entropy encoded and sent as side information, Finally, the residual vectors are grouped according to their classes and undergo Karhunen-Loeve transforming in the spectral domain and discrete cosine transforming in the spatial domain. Numerical experiments on a six-band thematic mapper image show that the proposed technique outperforms the conventional transform coding technique by 1 to 2 dB at all rates of interest.     

Model-based coding of videophone images

The requirement for improved picture quality in videophone and videoconference systems operating at low bit-rates has stimulated interest in model-based image coding. Two model-based coding techniques are described which are capable of producing either improved picture quality at bit-rates around 64 kbit/s or acceptable picture quality at bit-rates far lower than 64 kbit/s. The first technique produces facial expressions by using feature code-books; the second technique produces facial expressions by distorting an underlying three-dimensional model. The problems of image analysis and synthesis, which are concomitant in model-based coding, are discussed     

Circuit noise reduction by analogue lowpass filtering and dataaveraging

All electrical signals are, to some extent, contaminated by noise. Two popular techniques for eliminating noise are analogue lowpass filtering and data averaging. Here, consideration is given to the cutoff frequency of analogue lowpass filters, and the minimum number of data samples needed to be averaged in order to reduce noise with known autocorrelation function within a predefined limit. The time delays introduced by both analogue lowpass filtering and data averaging are given. Finally, the signal-to-noise ratio, the averaging technique used in a capacitance/resistance spectroscopic tomography system design and their effect on measurement accuracy and data acquisition rate are analysed     

Multiscale MAP filtering of SAR images

Synthetic aperture radar (SAR) images are disturbed by a multiplicative noise depending on the signal (the ground reflectivity) due to the radar wave coherence. Images have a strong variability from one pixel to another reducing essentially the efficiency of the algorithms of detection and classification. We propose to filter this noise with a multiresolution analysis of the image. The wavelet coefficient of the reflectivity is estimated with a Bayesian model, maximizing the a posteriori probability density function. The different probability density function are modeled with the Pearson system of distributions. The resulting filter combines the classical adaptive approach with wavelet decomposition where the local variance of high-frequency images is used in order to segment and filter wavelet coefficients.     

现代图像压缩编码技术

阐述了图像压缩编码的基本原理，系统地介绍了几种比较有应用前景的现代图像编码方法及其特点，最后对图像编码进行了总结和展望，指出从图像模型的角度研究图像编码将成为新一代图像编码的研究方向。     

Deblurring of color images corrupted by impulsive noise.

We consider the problem of restoring a multichannel image corrupted by blur and impulsive noise (e.g., salt-and-pepper noise). Using the variational framework, we consider the L1 fidelity term and several possible regularizers. In particular, we use generalizations of the Mumford-Shah (MS) functional to color images and gamma-convergence approximations to unify deblurring and denoising. Experimental comparisons show that the MS stabilizer yields better results with respect to Beltrami and total variation regularizers. Color edge detection is a beneficial by-product of our methods.     

利用偏振滤波实现黑白和彩色图像增强

本文提出一种利用偏振滤波法实现零频衰减从而获得图像的边缘增强的方法。制作简单,且可连续控制边缘增强图像的对比度。     

Interframe coding of magnetic resonance images

Presents a new interframe coding method for medical images, in particular magnetic resonance (MR) images. Until now, attempts in using interframe redundancies for coding MR images have been unsuccessful. The authors believe that the main reason for this is twofold: unsuitable interframe estimation models and the thermal noise inherent in magnetic resonance imaging (MRI). The interframe model used here is a continuous affine mapping based on (and optimized by) deforming triangles. The inherent noise of MRI is dealt with by using a median filter within the estimation loop. The residue frames are quantized with a zero-tree wavelet coder, which includes arithmetic entropy coding. This particular method of quantization allows for progressive transmission, which aside from avoiding buffer control problems is very attractive in medical imaging applications.     

VQ-adaptive block transform coding of images

Two new design techniques for adaptive orthogonal block transforms based on vector quantization (VQ) codebooks are presented. Both techniques start from reference vectors that are adapted to the characteristics of the signal to be coded, while using different methods to create orthogonal bases. The resulting transforms represent a signal coding tool that stands between a pure VQ scheme on one extreme and signal-independent, fixed block transformation-like discrete cosine transform (DCT) on the other. The proposed technique has superior compaction performance as compared to DCT both in the rendition of details of the image and in the peak signal-to-noise ratio (PSNR) figures.     

Orientation adaptive subband coding of images

In the subband coding of images, directionality of image features has thus far been exploited very little. The proposed subband coding scheme utilizes orientation of local image features to avoid the highly objectionable Gibbs-like phenomena observed at reconstructed image edges with conventional subband schemes at low bit rates, At comparable bit rates, the subjective image quality obtained by our orientation adaptive scheme is considerably enhanced over a conventional separable subband coding scheme, as well as other separable approaches such as the JPEG compression standard.