Showing papers by "Karen Egiazarian published in 2008"

Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data

Abstract: We present a simple and usable noise model for the raw-data of digital imaging sensors This signal-dependent noise model, which gives the pointwise standard-deviation of the noise as a function of the expectation of the pixel raw-data output, is composed of a Poissonian part, modeling the photon sensing, and Gaussian part, for the remaining stationary disturbances in the output data We further explicitly take into account the clipping of the data (over- and under-exposure), faithfully reproducing the nonlinear response of the sensor We propose an algorithm for the fully automatic estimation of the model parameters given a single noisy image Experiments with synthetic images and with real raw-data from various sensors prove the practical applicability of the method and the accuracy of the proposed model

Image restoration by sparse 3D transform-domain collaborative filtering

Abstract: We propose an image restoration technique exploiting regularized inversion and the recent block-matching and 3D filtering (BM3D) denoising filter. The BM3D employs a non-local modeling of images by collecting similar image patches in 3D arrays. The so-called collaborative filtering applied on such a 3D array is realized by transformdomain shrinkage. In this work, we propose an extension of the BM3D filter for colored noise, which we use in a two-step deblurring algorithm to improve the regularization after inversion in discrete Fourier domain. The first step of the algorithm is a regularized inversion using BM3D with collaborative hard-thresholding and the seconds step is a regularized Wiener inversion using BM3D with collaborative Wiener filtering. The experimental results show that the proposed technique is competitive with and in most cases outperforms the current best image restoration methods in terms of improvement in signal-to-noise ratio.

Color image database for evaluation of image quality metrics

Abstract: In this contribution, a new image database for testing full-reference image quality assessment metrics is presented. It is based on 1700 test images (25 reference images, 17 types of distortions for each reference image, 4 levels for each type of distortion). Using this image database, 654 observers from three different countries (Finland, Italy, and Ukraine) have carried out about 400000 individual human quality judgments (more than 200 judgments for each distorted image). The obtained mean opinion scores for the considered images can be used for evaluating the performances of visual quality metrics as well as for comparison and for the design of new metrics. The database, with testing results, is freely available.

Metrics performance comparison for color image database

Abstract: In this paper, we exploit a new database of distorted test images TID2008 for verification of full-reference metrics of image visual quality A comparative analysis of TID20008 and its nearest analog LIVE Database is presented For a wide variety of known metrics, their correspondence to human visual system is evaluated The values of rank correlations of Spearman and Kendall with the considered metrics and Mean Opinion Score (MOS) obtained by exploiting TID2008 in experiments are presented The metrics are verified for both full set of distorted test images in TID2008 (1700 distorted images, 17 types of distortions) and for particular subsets of TID2008 that include distortions most important for digital image processing applications

Absolute phase estimation: adaptive local denoising and global unwrapping

Abstract: The paper attacks absolute phase estimation with a two-step approach: the first step applies an adaptive local denoising scheme to the modulo-2π noisy phase; the second step applies a robust phase unwrapping algorithm to the denoised modulo-2π phase obtained in the first step. The adaptive local modulo-2π phase denoising is a new algorithm based on local polynomial approximations. The zero-order and the first-order approximations of the phase are calculated in sliding windows of varying size. The zero-order approximation is used for pointwise adaptive window size selection, whereas the first-order approximation is used to filter the phase in the obtained windows. For phase unwrapping, we apply the recently introduced robust (in the sense of discontinuity preserving) PUMA unwrapping algorithm [IEEE Trans. Image Process.16, 698 (2007)] to the denoised wrapped phase. Simulations give evidence that the proposed algorithm yields state-of-the-art performance, enabling strong noise attenuation while preserving image details.

Phase Local Approximation ( PhaseLa ) Technique for Phase Unwrap From Noisy Data

Abstract: The local polynomial approximation (LPA) is a nonparametric regression technique with pointwise estimation in a sliding window. We apply the LPA of the argument of cos and sin in order to estimate the absolute phase from noisy wrapped phase data. Using the intersection of confidence interval (ICI) algorithm, the window size is selected as adaptive pointwise varying. This adaptation gives the phase estimate with the accuracy close to optimal in the mean squared sense. For calculations, we use a Gauss-Newton recursive procedure initiated by the phase estimates obtained for the neighboring points. It enables tracking properties of the algorithm and its ability to go beyond the principal interval (-pi,pi) and to reconstruct the absolute phase from wrapped phase observations even when the magnitude of the phase difference takes quite large values. The algorithm demonstrates a very good accuracy of the phase reconstruction which on many occasion overcomes the accuracy of the state-of-the-art algorithms developed for noisy phase unwrap. The theoretical analysis produced for the accuracy of the pointwise estimates is used for justification of the ICI adaptation algorithm.

Application of Bispectrum Estimation for Time-Frequency Analysis of Ground Surveillance Doppler Radar Echo Signals

Abstract: A microwave coherent homodyne and polarimetric ground surveillance Doppler radar is employed for collecting the radar returns from moving objects. Nonstationary nonlinearly frequency-modulated and multicomponent backscattered signals are analyzed and described as a sum of Doppler frequency-shifted polynomial chirp-like components. Instantaneous frequencies corresponding to the radiation backscattered by the different parts of a moving spatially distributed object are extracted from the time-varying bimagnitude estimates of transient sample sequences separated from the total received signal by a sliding window and projected into the time-frequency (TF) domain. Experimental investigations demonstrate a clean recovery of evolutionary phase-coupled harmonics for such targets as a swinging metallic sphere or a walking human. The computed TF distributions can be used in radar automatic target recognition systems to retrieve new data for the classification and recognition of ground moving objects.

Adaptive DCT-based filtering of images corrupted by spatially correlated noise

Abstract: Majority of image filtering techniques are designed under assumption that noise is of special, a priori known type and it is i.i.d., i.e. spatially uncorrelated. However, in many practical situations the latter assumption is not true due to several reasons. Moreover, spatial correlation properties of noise might be rather different and a priori unknown. Then the assumption that noise is i.i.d. under real conditions of spatially correlated noise commonly leads to considerable decrease of a used filter effectiveness in comparison to a case if this spatial correlation is taken into account. Our paper deals with two basic aspects. The first one is how to modify a denoising algorithm, in particular, a discrete cosine transform (DCT) based filter in order to incorporate a priori or preliminarily obtained knowledge of spatial correlation characteristics of noise. The second aspect is how to estimate spatial correlation characteristics of noise for a given image with appropriate accuracy and robustness under condition that there is some a priori information about, at least, noise type and statistics like variance (for additive noise case) or relative variance (for multiplicative noise). We also present simulation results showing the effectiveness (the benefit) of taking into consideration noise correlation properties.

Discrete diffraction transform for propagation, reconstruction, and design of wavefield distributions.

Abstract: A discrete diffraction transform (DDT) is a novel discrete wavefield propagation model that is aliasing free for a pixelwise invariant object distribution. For this class of distribution, the model is precise and has no typical discretization effects because it corresponds to accurate calculation of the diffraction integral. A spatial light modulator (SLM) is a good example of a system where a pixelwise invariant distribution appears. Frequency domain regularized inverse algorithms are developed for reconstruction of the object wavefield distribution from the distribution given in the sensor plane. The efficiency of developed frequency domain algorithms is demonstrated by simulation.

Image upsampling via spatially adaptive block-matching filtering

Abstract: In this paper we present a novel wavelet-domain image upsampling algorithm based on iterative spatially adaptive filtering. A high-resolution image is reconstructed by alternating two procedures: spatially adaptive filtering and projection on the observation-constrained subspace. The Block-Matching and 3D filtering (BM3D) [3] technique is used to suppress ringing, and reconstruct missing wavelet detail coefficients. The BM3D algorithm exploits the local image statistics collected from similar blocks to extract local and non-local image features by 3D transform-domain shrinkage. It results in high-quality upsampled images, with sharp edges and practically no artifacts.

Lossless and near lossless compression of real color filter array data

Abstract: Compression of Bayer pattern color filter array (CFA) data has gained a lot of attention during past years. Numerous algorithms have been proposed for lossless, near-lossless and lossy compression. The performance evaluation of compression methods is typically done only for artificial CFA data, obtained by sub-sampling full color images according to CFA pattern, without taking into account that CFA data are heavily processed before obtaining full color images. Therefore, some assumptions that are true for reconstructed images may not hold for real raw data. Thus, compression efficiency of different methods may vary. In this paper we evaluate the performance of some methods for lossless and near-lossless compression for real raw Bayer pattern CFA data obtained from digital cameras.

Lossy compression of multichannel ECG based on 2-D DCT and pre-processing

Abstract: We propose and analyze a novel efficient technique for lossy compression of multichannel ECG records that provides a reasonable trade-off between distortions introduced and compression ratio provided. This technique exploits inter-channel correlation of signals in cardio-complex leads by means of using a 2-D DCT and special operations of data pre-processing that improve compression performance. Real practical examples proving the proposed coder efficiency are presented.

GPU-based algorithms for optimized visualization and crosstalk mitigation on a multiview display

Abstract: In this contribution, we present two GPU-optimized algorithms for displaying the frames of 2D-plus-Z stream on a multiview 3D display. We aim at mitigating the cross-talk artifacts, which are inherent for such displays. In our approach, a 3D mesh is generated using the given depth map, then textured by the given 2D scene and properly interdigitized on the screen. We make use of the GPU built-in libraries to perform these operations in a fast manner. To reduce the global crosstalk presence, we investigate two approaches. In the first approach, the 2D image is appropriately smoothed before texturing. The smoothing is done in horizontal direction by a 1-D filter bank driven by the given depth map. Such smoothing provides the needed anti-aliasing at the same filtering step. In the second approach, we introduce a higher number of properly blended virtual views than the display views supported and demonstrate that this is equivalent to a smoothing operation. We provide experimental results and discuss the performance and computational complexity of the two approaches. While the first approach is more appropriate for higher-resolution displays equipped with newer graphical accelerators, the latter approach is more general and suitable for lower-resolution displays and wider range of graphic accelerators.

Optimized single-viewer mode of multiview autostereoscopic display

Abstract: We propose an approach for optimizing the visual quality of a multiview 3D display for a single viewer. The approach combines eye-position tracking system with on-the-fly visual optimization of multiview image content. The tracking algorithm uses the video input from a pair of off-the-shelf web-cameras and employs fast and robust face and facial feature detection algorithms to provide features for the subsequent stereo matching and distance estimation. Based on display measurements and having user's eyes position, the following visual improvements are achieved: continuous head parallax for wide range of observation angles, cross-talk mitigation and brightness enhancement for a single viewer, and multi-view image compensation related to the distance of the observer.

Deblurring noisy radial-blurred images: spatially adaptive filtering approach

Abstract: The deblurring of images corrupted by radial blur is studied. This type of blur appears in images acquired during an any camera translation having a substantial component orthogonal to the image plane. The point spread functions (PSF PSF) describing this blur are spatially varying. However, this blurring process does not mix together pixels lying on differen different radial lines, i.e. lines stemming from a unique point in the image, the so called "blur center". Thus, in suitable pola polar coordinates, the blurring process is essentially a 1-D linear operator, described by the multiplication with the blurrin blurring matrix. We consider images corrupted simultaneously by radial blur and noise. The proposed deblurring algorithm is base based on two separate forms of regularization of the blur inverse. First, in the polar domain, we invert the blurring matri matrix using the Tikhonov regularization. We then derive a particular modeling of the noise spectrum after both the regularize regularized inversion and the forward and backward coordinate transformations. Thanks to this model, we successfully use a denoisin denoising algorithm in the Cartesian domain. We use a non-linear spatially adaptive filter, the Pointwise Shape-Adaptive DCT, i in order to exploit the image structures and attenuate noise and artifacts. Experimental results demonstrate that the proposed algorithm can effectively restore radial blurred images corrupted by additive white Gaussian noise.

A spatial data hiding scheme based on generalized Fibonacci sequence

Abstract: This paper presents a novel spatial data hiding scheme based on the Least Significant Bit insertion. The bitplane decomposition is obtained by using the (p, r) Fibonacci sequences. This decomposition depends on two parameters, p and r. Those values increase the security of the whole system; without their knowledge it is not possible to perform the same decomposition used in the embedding process and to extract the embedded information. Experimental results show the effectiveness of the proposed method.

Joint deblurring and demosaicing of Poissonian Bayer-data based on local adaptivity

Abstract: We present a novel technique for joint deblurring and demosaicing of noisy Poissonian Bayer data (e.g., data acquired by a digital CMOS or CCD imaging sensor). The technique incorporates the regularized inverse and the Wiener inverse with adaptive filtering based on the concept of cross-color local polynomial approximation (LPA) and intersection of confidence intervals (ICI). The directional filters designed by LPA utilize simultaneously the green, red, and blue color components. This is achieved by a linear combination of complementary-supported smoothing and derivative kernels designed for the Bayer data grid. The ICI rule is used for data-adaptive selection of the length of the designed cross-color directional filter. Simulation experiments demonstrate the efficiency of the proposed technique with respect to the conventional approach where deconvolution and demosaicing are computed independently.

Opengl-Based Control of Semi-Active 3D Display

Abstract: We present a system for 3D visualisation, which combines user-tracking, used by displays with steerable optics, with generation of multiple views, typical for displays with fixed optical filter. Instead of eye-tracking, typical for the user- tracking approach, we propose a less computationally demanding head tracking, based on face detection. We investigate if the precise delivery of different images to each eye of the observer can be handled by the fixed optics of a multiview 3D display, and if continuous head parallax can be achieved.

Wavefield Reconstruction and Design as Discrete Inverse Problems

Abstract: A novel discrete model for the wavefield propagation is proposed This model is accurate for a pixel-wise constant object distribution, ie for a object distribution which is piece-wise invariant on rectangular elements of pixel's size of a digital hologram CCD sensor We apply this model for two different inverse problems: reconstruction of an object complex-valued distribution from holographic data and design of a digital hologram giving a desirable distribution as result of the wavefield propagation The efficiency and advantage of the developed algorithm are demonstrated

An image registration technique aimed at super resolution on mobile devices

Abstract: We propose an image registration technique to be implemented on mobile devices equipped with cameras. We address the limited computational power and low-quality optics of such devices and aim at designing a registration algorithm, which is fast, robust with respect to noise, and allows for corrections of optical distortions. We favor a feature-based approach, consisting of feature extraction, feature filtering, feature matching, and transformation estimation. In our application, the transformation estimation is robust to local distortions, and is accurate enough to allow for a subsequent super-resolution on the registered images. The performance of the technique is demonstrated in fixed-point implementation on the TMS 320 C5510 DSP.

Hybrid Reed-Muller Haar Transform and its Application in Reduction the Spectral Representations of Logic Functions

Abstract: In this paper we present a new hybrid transform based on the Kronecker product of Reed-Muller and Reed-Muller Haar transforms. The proposed transform shares attractive properties of both Reed-Muller transform and Reed-Muller Haar transform. An example of application of hybrid transform for reduction of the number of nonzero coefficients in spectra of truth vectors of switching functions is presented. The experiments show that the proposed approach, on average, reduces the number of nonzero coefficients in the spectra of benchmark functions.

Digital wavefield reconstruction in phase-shifting holography as inverse discrete problem

Abstract: A novel discrete model for the wavefield propagation is proposed. This model is accurate (and aliasing free) for a pixel-wise constant object distribution which is invariant on rectangular elements of pixel's size of a digital hologram CCD sensor. The sizes of object and sensor arrays can be different. A spacial light modulator (SLM) is a good example of the object where a pixel-wise invariant distribution appears. We consider reconstruction of the object complex-valued distribution from the phase-shifting holography data as an inverse discrete problem. The reconstruction becomes more accurate when the sensor size is larger than the size of the object aperture. An efficient frequency domain algorithm is demonstrated.

Image filter effectiveness characterization based on HVS

Abstract: It is a quite common that acquired images are noisy and image filtering is a necessary step to enhance them. Usually image filtering effectiveness is characterized in terms of MSE or PSNR although nowadays it is well understood that these criteria do not always correspond adequately to visual perception of processed images. Recently several new measures of image quality have been proposed. In particular, two metrics, called PSNR-HVS and PSNR-HVS-M, were designed and successfully tested 16,17 . Both take into account different sensitivity of a human eye to spatial frequencies, the latter one also accounts for the masking effects. Using th ese two metrics as well as a traditional PSNR and used by NASA metric DCTune, we have analyzed performance of five different filters (standard mean and median, sigma, Lee and DCT based filters) for a set of test images corrupted by an additive Gaussian noise with a wide set of variance val-ues. It has been shown that there are many situations when PSNR after filtering improves while one or all other metrics manifest image quality decreasing. Most often this happens if noise variance is small and/or an image contains texture. Comparisons show that DCT based filter commonly outperforms other considered filters in the sense of denoised image visual quality. At the same time, the standard mean filter produces worse visual quality of processed images even its scanning window size is 3x3. Keywords : additive noise, image filtering, perceptual quality.

Signal Reconstruction by Using Normalized Bispectrum Estimates in Noise

Abstract: The problem of reconstructing an unknown waveform observed in additive noise by using normalized bispectral density estimates is considered. The proposed approach is based on using a continuous-valued normalized bispectral density estimate instead of the discontinuous biphase function conventionally computed in bispectrum-based signal reconstruction algorithms. The performance and reconstruction accuracy of the developed technique are studied by computer simulations in the presence of additive Gaussian and impulsive noises. Computer simulations results demonstrate that improvement of the signal reconstruction accuracy is achieved by the suggested approach.

Adaptive Local Phase Approximations and Global Unwrapping

Abstract: The paper introduces a new modulo-2pi phase denoising algorithm based on local polynomial approximations. The zero and first order approximations of the phase are calculated in sliding windows of varying size. The former is used for point- wise adaptive window size selection, while the latter is used for filtering the phase in the obtained windows. For unwrapping, we input the PUMA unwrapping algorithm [1] with the denoised wrapped phase obtained with the proposed approach. Simulation shows that this technique enables strong noise attenuation while preserving image details.

As inverse discrete problem

Abstract: A novel discrete model for the wavefield propagation is pro­ posed. This model is accurate (and aliasing free) for a pixel­ wise constant object distribution which is invariant on rec­ tangular elements of pixel's size of a digital hologram CC D sensor. The sizes of object and sensor arrays can be different. A spacial light modulator (SL M) is a good example of the object where a pixel-wise invariant distribution appears. We consider reconstruction of the object complex-valued distri­ bution from the phase-shifting holography data as an inverse discrete problem. The reconstruction becomes more accu­ rate when the sensor size is larger than the size of the object aperture. An efficient frequency domain algorithm is demon­ strated.