Browsing by Author "Neelamani, Ramesh"
Now showing 1 - 20 of 25
Results Per Page
Sort Options
Item Adaptive Representation of JPEG 2000 Images Using Header-based Processing(2002-09-20) Neelamani, Ramesh; Berkner, Kathrin; Center for Multimedia Communications (http://cmc.rice.edu/); Digital Signal Processing (http://dsp.rice.edu/); CML (http://cml.rice.edu/); CITI (http://citi.rice.edu/)To bridge the mismatch between the sizes of images and display devices, we present an efficient and automatic algorithm to create an adaptive image representation called SmartNail. Given a digital image and rectangular display frame smaller than the image, we define the SmartNail as an appropriately cropped part of a suitably scaled-down image. We choose the SmartNail-defining parameters - down-scaling factor and cropping location - to maximize a bit-allocation-based cost function that quantifies the visual importance of the image content in the SmartNail. For JPEG 2000-encoded images, the SmartNail parameters can be determined using just the header information available in the encoded file. Hence only the wavelet coefficients required to reconstruct the SmartNail need to be decoded from the entire JPEG 2000 code stream. Consequently, the SmartNail construction requires minimal computations and memory requirements. Simulations demonstrate the effectiveness of SmartNail representations.Item Applications of Terahertz Imaging(1998-08-01) Mittleman, Daniel M.; Neelamani, Ramesh; Baraniuk, Richard G.; Nuss, Martin C.; Digital Signal Processing (http://dsp.rice.edu/)The recent advances involving imaging with sub-picosecond terahertz pulses have opened up a wide range of possibilities in the applications of far-infrared technology. For the first time, a commercially viable terahertz imaging spectrometer seems a realizable prospect. However, several substantial engineering research challenges remain to be overcome before this goal can be achieved. One of these involves the necessity for a femtosecond laser system, required for gating the emitter and receiver antennas used in the THz-TDS system. The demonstration experiments performed to date have employed rather crude signal processing algorithms. The shortcomings of these are evident in some of the results presented here, highlighting the need for a more sophisticated treatment.Item Compression Color Space Estimation of JPEG Images using Lattice Basis Reduction(2001-10-01) Neelamani, Ramesh; Baraniuk, Richard G.; de Queiroz, Ricardo; Center for Multimedia Communications (http://cmc.rice.edu/); Digital Signal Processing (http://dsp.rice.edu/)Given a color image that was previously JPEG-compressed in some hidden color space, we aim to estimate this unknown compression color space from the image. This knowledge is potentially useful for color image enhancement and JPEG recompression. JPEG operates on the discrete cosine transform (DCT) coefficients of each color plane independently during compression. Consequently, the DCT coefficients conform to a lattice structure. We exploit this special geometry using the lattice reduction algorithm from number theory and cryptography to estimate the compression color space. Simulations verify that the proposed algorithm yields accurate compression color space estimates.Item The Embedded Triangles Algorithm for Distributed Estimation in Sensor Networks(2003-09-01) Delouille, Veronique; Neelamani, Ramesh; Chandrasekaran, Venkat; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)We propose a new iterative distributed estimation algorithm for Gaussian hidden Markov graphical models with loops. We decompose a loopy graph into a number of linked embedded triangles and then apply a parallel block-Jacobi iteration comprising local linear minimum mean-square-error estimation on each triangle (involving a simple 3 × 3 matrix inverse computation) followed by an information exchange between neighboring nodes and triangles. A simulation study demonstrates that the algorithm converges extremely rapidly, outperforming a number of existing algorithms. Embedded triangles are simple, local, scalable, fault-tolerant, and energy-efficient, and thus ideally suited for wireless sensor networks.Item ForWaRD: Fourier-Wavelet Regularized Deconvolution for Ill-Conditioned Systems(2004-02-01) Neelamani, Ramesh; Choi, Hyeokho; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)We propose an efficient, hybrid Fourier-Wavelet Regularized Deconvolution (ForWaRD) algorithm that performs noise regularization via scalar shrinkage in both the Fourier and wavelet domains. The Fourier shrinkage exploits the Fourier transform's sparse representation of the colored noise inherent in deconvolution, while the wavelet shrinkage exploits the wavelet domain's sparse representation of piecewise smooth signals and images. We derive the optimal balance between the amount of Fourier and wavelet regularization by optimizing an approximate mean-squared-error (MSE) metric and find that signals with sparser wavelet representations require less Fourier shrinkage. ForWaRD is applicable to all ill-conditioned deconvolution problems, unlike the purely wavelet-based Wavelet-Vaguelette Deconvolution (WVD), and its estimate features minimal ringing, unlike purely Fourier-based Wiener deconvolution. We analyze ForWaRD's MSE decay rate as the number of samples increases and demonstrate its improved performance compared to the optimal WVD over a wide range of practical sample-lengths.Item Gas Sensing using Terahertz Time-Domain Spectroscopy(1998-01-15) Mittleman, Daniel M.; Jacobsen, R.H.; Neelamani, Ramesh; Baraniuk, Richard G.; Nuss, Martin C.; Center for Multimedia Communications (http://cmc.rice.edu/); Digital Signal Processing (http://dsp.rice.edu/)A method for detection and identification of polar gases and gas mixtures based on the technique of terahertz time-domain spectroscopy is presented. This relatively new technology promises to be the first portable far-infared spectrometer, providing a means for real-time spectroscopic measurements over a broad bandwidth up to several THz. The measured time-domain waveforms can be efficiently parameterized using standard tools from signal processing, including procedures developed for speech recognition applications. These are generally more efficient than conventional methods based on Fourier analysis, and are easier to implement in a real-time sensing system. Preliminary results of real-time gas mixtures analysis using a linear predictive coding algorithm are presented. A number of possible avenues for improved signal processing schemes are discussed. In particular, the utility of a wavelet-based signal analysis for tasks such as denoising is demonstrated.Item Inverse problems in image processing(2004) Neelamani, Ramesh; Baraniuk, Richard G.Inverse problems involve estimating parameters or data from inadequate observations; the observations are often noisy and contain incomplete information about the target parameter or data due to physical limitations of the measurement devices. Consequently, solutions to inverse problems are non-unique. To pin down a solution, we must exploit the underlying structure of the desired solution set. In this thesis, we formulate novel solutions to three image processing inverse problems: deconvolution, inverse halftoning, and JPEG compression history estimation for color images. Deconvolution aims to extract crisp images from blurry observations. We propose an efficient, hybrid Fourier-Wavelet Regularized Deconvolution (ForWaRD) algorithm that comprises blurring operator inversion followed by noise attenuation via scalar shrinkage in both the Fourier and wavelet domains. The Fourier shrinkage exploits the structure of the colored noise inherent in deconvolution, while the wavelet shrinkage exploits the piecewise smooth structure of real-world signals and images. ForWaRD yields state-of-the-art mean-squared-error (MSE) performance in practice. Further, for certain problems, ForWaRD guarantees an optimal rate of MSE decay with increasing resolution. Halftoning is a technique used to render gray-scale images using only black or white dots. Inverse halftoning aims to recover the shades of gray from the binary image and is vital to process scanned images. Using a linear approximation model for halftoning, we propose the Wavelet-based Inverse Halftoning via Deconvolution (WInHD) algorithm. WInHD exploits the piecewise smooth structure of real-world images via wavelets to achieve good inverse halftoning performance. Further, WInHD also guarantees a fast rate of MSE decay with increasing resolution. We routinely encounter digital color images that were previously JPEG-compressed. We aim to retrieve the various settings---termed JPEG compression history---employed during previous JPEG operations. This information is often discarded en-route to the image's current representation. We discover that the previous JPEG compression's quantization step introduces lattice structures into the image. Our study leads to a fundamentally new result in lattice theory---nearly orthogonal sets of lattice basis vectors contain the lattice's shortest non-zero vector. We exploit this insight along with other known, novel lattice-based algorithms to effectively uncover the image's compression history. The estimated compression history significantly improves JPEG recompression.Item JPEG Compression History Estimation for Color Images(2006) Neelamani, Ramesh; de Queiroz, Ricardo; Fan, Zhigang; Dash, Sanjeeb; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)We routinely encounter digital color images that were previously JPEG-compressed. En route to the image's current representation, the previous JPEG compression's various settings—termed its JPEG compression history (CH)—are often discarded after the JPEG decompression step. Given a JPEG-decompressed color image, this paper aims to estimate its lost JPEG CH. We observe that the previous JPEG compression's quantization step introduces a lattice structure in the discrete cosine transform (DCT) domain. This paper proposes two approaches that exploit this structure to solve the JPEG Compression History Estimation (CHEst) problem. First, we design a statistical dictionary-based CHEst algorithm that tests the various CHs in a dictionary and selects the maximum a posteriori estimate. Second, for cases where the DCT coefficients closely conform to a 3-D parallelepiped lattice, we design a blind lattice-based CHEst algorithm. The blind algorithm exploits the fact that the JPEG CH is encoded in the nearly orthogonal bases for the 3-D lattice and employs novel lattice algorithms and recent results on nearly orthogonal lattice bases to estimate the CH. Both algorithms provide robust JPEG CHEst performance in practice. Simulations demonstrate that JPEG CHEst can be extremely useful in JPEG recompression; the estimated CH allows us to recompress a JPEG-decompressed image with minimal distortion (large signal-to-noise-ratio) and simultaneously achieve a small file-size.Item JPEG Compression History Estimation for Color Images(2006-06-01) Neelamani, Ramesh; de Queiroz, Ricardo; Fan, Zhigang; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)We routinely encounter digital color images that were previously compressed using the Joint Photographic Experts Group (JPEG) standard. En route to the image's current representation, the previous JPEG compression's various settingsâ termed its JPEG compression history (CH)â are often discarded after the JPEG decompression step. Given a JPEG-decompressed color image, this paper aims to estimate its lost JPEG CH. We observe that the previous JPEG compression's quantization step introduces a lattice structure in the discrete cosine transform (DCT) domain. This paper proposes two approaches that exploit this structure to solve the JPEG Compression History Estimation (CHEst) problem. First, we design a statistical dictionary-based CHEst algorithm that tests the various CHs in a dictionary and selects the maximum a posteriori estimate. Second, for cases where the DCT coefficients closely conform to a 3-D parallelepiped lattice, we design a blind lattice-based CHEst algorithm. The blind algorithm exploits the fact that the JPEG CH is encoded in the nearly orthogonal bases for the 3-D lattice and employs novel lattice algorithms and recent results on nearly orthogonal lattice bases to estimate the CH. Both algorithms provide robust JPEG CHEst performance in practice. Simulations demonstrate that JPEG CHEst can be useful in JPEG recompression; the estimated CH allows us to recompress a JPEG-decompressed image with minimal distortion (large signal-to-noise-ratio) and simultaneously achieve a small file-size.Item JPEG Compression History Estimation for Color Images(2003-09-01) Neelamani, Ramesh; de Queiroz, Ricardo; Fan, Zhigang; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)We routinely encounter digital color images that were previously JPEG-compressed. We aim to retrieve the various settings - termed JPEG compression history (CH) - employed during previous JPEG operations. This information is often discarded en-route to the image's current representation. The discrete cosine transform coefficient histograms of previously JPEG-compressed images exhibit near-periodic behavior due to quantization. We propose a statistical approach to exploit this structure and thereby estimate the image's CH. Using simulations, we first demonstrate the accuracy of our estimation. Further, we show that JPEG recompression performed by exploiting the estimated CH strikes an excellent file-size versus distortion tradeoff.Item Lattice Algorithms for Compression Color Space Estimation in JPEG Images(2001-08-01) Neelamani, Ramesh; de Queiroz, Ricardo; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)JPEG (Joint Photographic Experts Group) is an international standard to compress and store digital color images [5]. Given a color image that was previously JPEG-compressed in some hidden color space, we aim to estimate this unknown compression color space from the image. This knowledge is potentially useful for color image enhancement and JPEG re-compression. JPEG operates on the discrete cosine transform (DCT) coefficients of each color plane independently during compression. Consequently, the DCT coefficients of the color image conform to a lattice structure. We exploit this special geometry using the lattice reduction algorithm from number theory and cryptography to estimate the compression color space. Simulations verify that the proposed algorithm yields accurate compression color space estimates.Item Model-based Inverse Halftoning with Wavelet-Vaguelette Deconvolution(2000-09-01) Neelamani, Ramesh; Nowak, Robert David; Baraniuk, Richard G.; Center for Multimedia Communications (http://cmc.rice.edu/); Digital Signal Processing (http://dsp.rice.edu/)In this paper, we demonstrate based on the linear model of Kite that inverse halftoning is equivalent to the well-studied problem of deconvolution in the presence of colored noise. We propose the use of the simple and elegant wavelet-vaguelette deconvolution (WVD) algorithm to perform the inverse halftoning. Unlike previous wavelet-based algorithms, our method is model-based; hence it is adapted to different error diffusion halftoning techniques. Our inverse halftoning algorithm consists of inverting the convolution operator followed by denoising in the wavelet domain. For signals in a Besov space, our algorithm possesses asymptotically (as the number of samples nears infinity near-optimal rates of error decay. Hence for images in a Besov space, it is impossible to improve significantly on the inverse halftoning performance of the WVD algorithm at high resolutions. Using simulations, we verify that our algorithm outperforms or matches the performances of the best published inverse halftoning techniques in the mean square error (MSE) sense and also provides excellent visual performance.Item Multiscale Image Segmentation Using Joint Texture and Shape Analysis(2000-07-01) Neelamani, Ramesh; Romberg, Justin; Riedi, Rudolf H.; Choi, Hyeokho; Baraniuk, Richard G.; Center for Multimedia Communications (http://cmc.rice.edu/); Digital Signal Processing (http://dsp.rice.edu/)We develop a general framework to simultaneously exploit texture and shape characterization in multiscale image segmentation. By posing multiscale segmentation as a model selection problem, we invoke the powerful framework offered by minimum description length (MDL). This framework dictates that multiscale segmentation comprises multiscale texture characterization and multiscale shape coding. Analysis of current multiscale maximum a posteriori (MAP) segmentation algorithms reveals that these algorithms implicitly use a shape coder with the aim to estimate the optimal MDL solution, but find only an approximate solution. Towards achieving better segmentation estimates, we first propose a shape coding algorithm based on zero-trees which is well-suited to represent images with large homogeneous regions. For this coder, we design an efficient tree-based algorithm using dynamic programming that attains the optimal MDL segmentation estimate. To incorporate arbitrary shape coding techniques into segmentation, we design an iterative algorithm that uses dynamic programming for each iteration. Though the iterative algorithm is not guaranteed to attain exactly optimal estimates, it more effectively captures the prior set by the shape coder. Experiments demonstrate that the proposed algorithms yield excellent segmentation results on both synthetic and real world data examples.Item On Nearly Orthogonal Lattice Bases(2005-07-01) Neelamani, Ramesh; Dash, Sanjeeb; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)We study "nearly orthogonal" lattice bases, or bases where the angle between any basis vector and the linear subspace spanned by the other basis vectors is greater than 60°. We show that a nearly orthogonal lattice basis always contains a shortest lattice vector. Moreover, if the lengths of the basis vectors are "nearly equal", then the basis is the unique nearly orthogonal lattice basis, up to multiplication of basis vectors by ±1. These results are motivated by an application involving JPEG image compression.Item Recent advances in Imaging and Spectroscopy with T-rays(1998-12-01) Mittleman, Daniel M.; Neelamani, Ramesh; Gupta, Maya; Baraniuk, Richard G.; Nuss, Martin C.; Digital Signal Processing (http://dsp.rice.edu/)Recent work in terahertz "T-Ray" imaging is reported. With the ongoing development of commercially viable THz-TDS imaging system, opitmail signal processing strategies for the THz waveforms must be developed. Algorithms based on wavelet decomposition of the time and frequency-localized signals offer a number of advantages. Examples of denoising, deconvolution, and other waveform anaylsis tools are described.Item Recent Advances in Terahertz Imaging(1999-06-01) Koch, Martin; Rudd, J. Van; Neelamani, Ramesh; Gupta, Maya; Baraniuk, Richard G.; Mittleman, Daniel M.; Center for Multimedia Communications (http://cmc.rice.edu/); Digital Signal Processing (http://dsp.rice.edu/)We review recent progress in the field of terahertz "T-ray" imaging. This relatively new imaging technique, based on terahertz time-domain spectroscopy, has the potential to be the first portable far-infared imagin spectrometer. We give several examples whcih illustrate the possible applications of this technology, using both the amplitude and phase information contained in the THz waveforms. We desribe the latest results in tomographic imaging, in which waveforms reflected from an object can be used to form a three-dimensional representation. Advanced signal processing tools are exploted for the purpose of extracting tomographic results, including spectroscopic information about each reflecting layer of a sample. We also describe the applicatoin of optical near-field techniques to the THz imaging system. Substantial improvements in teh spatial resolution are demonstrated.Item Robust Distributed Estimation in Sensor Networks using the Embedded Polygons Algorithm(2004-04-01) Delouille, Veronique; Neelamani, Ramesh; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)We propose a new iterative distributed algorithm for linear minimum mean-squared-error (LMMSE) estimation in sensor networks whose measurements follow a Gaussian hidden Markov graphical model with cycles. The embedded polygons algorithm decomposes a loopy graphical model into a number of linked embedded polygons and then applies a parallel block Gauss-Seidel iteration comprising local LMMSE estimation on each polygon (involving inversion of a small matrix) followed by an information exchange between neighboring nodes and polygons. The algorithm is robust to temporary communication faults such as link failures and sleeping nodes and enjoys guaranteed convergence under mild conditions. A simulation study indicates that energy consumption for iterative estimation increases substantially as more links fail or nodes sleep. Thus, somewhat surprisingly, energy conservation strategies such as low-powered transmission and aggressive sleep schedules could actually be counterproductive.Item Robust Distributed Estimation Using the Embedded Subgraphs Algorithm(2006-08-01) Delouille, Veronique; Neelamani, Ramesh; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)We propose a new iterative, distributed approach for linear minimum mean-square-error (LMMSE) estimation in graphical models with cycles. The embedded subgraphs algorithm (ESA) decomposes a loopy graphical model into a number of linked embedded subgraphs and applies the classical parallel block Jacobi iteration comprising local LMMSE estimation in each subgraph (involving inversion of a small matrix) followed by an information exchange between neighboring nodes and subgraphs. Our primary application is sensor networks, where the model encodes the correlation structure of the sensor measurements, which are assumed to be Gaussian. The resulting LMMSE estimation problem involves a large matrix inverse, which must be solved in-network with distributed computation and minimal intersensor communication. By invoking the theory of asynchronous iterations, we prove that ESA is robust to temporary communication faults such as failing links and sleeping nodes, and enjoys guaranteed convergence under relatively mild conditions. Simulation studies demonstrate that ESA compares favorably with other recently proposed algorithms for distributed estimation. Simulations also indicate that energy consumption for iterative estimation increases substantially as more links fail or nodes sleep. Thus, somewhat surprisingly, sensor network energy conservation strategies such as low-powered transmission and aggressive sleep schedules could actually prove counterproductive. Our results can be replicated using MATLAB code from www.dsp.rice.edu/software.Item Wavelet-based deconvolution for ill-conditioned systems(2000-02-01) Neelamani, Ramesh; Choi, Hyeokho; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)We propose a hybrid approach to wavelet-based deconvolution that comprises Fourier-domain system inversion followed by wavelet-domain noise suppression. In contrast to other wavelet-based deconvolution approaches, the algorithm employs a regularized inverse filter, which allows it to operate even when the system is non-invertible. Using a mean-square-error (MSE) metric, we strike an optimal balance between Fourier-domain regularization (matched to the convolution operator) and wavelet-domain regularization (matched to the signal/image). Theoretical analysis reveals that the optimal balance is determined by the Fourier-domain operator structure and the economics of the wavelet-domain signal representation. The resulting algorithm is fast (O(N\log N) complexity for signals/images of N samples) and is well-suited to data with spatially-localized phenomena such as edges and ridges. In addition to enjoying asymptotically optimal rates of error decay for certain systems, the algorithm also achieves excellent performance at fixed data lengths. In real data experiments, the algorithm outperforms the conventional time-invariant Wiener filter and other wavelet-based image restoration algorithms in terms of both MSE performance and visual quality.Item Wavelet-based Deconvolution for Ill-conditioned Systems(1999-03-01) Neelamani, Ramesh; Choi, Hyeokho; Baraniuk, Richard G.; Center for Multimedia Communications (http://cmc.rice.edu/); Digital Signal Processing (http://dsp.rice.edu/)In this paper, we propose a new approach to wavelet-based deconvolution. Roughly speaking, the algorithm comprises Fourier-domain system inversion followed by wavelet-domain noise suppression. Our approach subsumes a number of other wavelet-based deconvolution methods. In contrast to other wavelet-based approaches, however, we employ a regularized inverse filter, which allows the algorithm to operate even when the inverse system is ill-conditioned or non-invertible. Using a mean-square-error metric, we strike an optimal balance between Fourier-domain and wavelet-domain regularization. The result is a fast deconvolution algorithm ideally suited to signals and images with edges and other singularities. In simulations with real data, the algorithm outperforms the LTI Wiener filter and other wavelet-based deconvolution algorithms in terms of both visual quality and MSE performance.