A super-resolution framework for 3-D high-resolution and high-contrast imaging using 2-D multislice MRI.

A novel super-resolution reconstruction (SRR) framework in magnetic resonance imaging (MRI) is proposed. Its purpose is to produce images of both high resolution and high contrast desirable for image-guided minimally invasive brain surgery. The input data are multiple 2-D multislice inversion recovery MRI scans acquired at orientations with regular angular spacing rotated around a common frequency encoding axis. The output is a 3-D volume of isotropic high resolution. The inversion process resembles a localized projection reconstruction problem. Iterative algorithms for reconstruction are based on the projection onto convex sets (POCS) formalism. Results demonstrate resolution enhancement in simulated phantom studies, and ex vivo and in vivo human brain scans, carried out on clinical scanners. A comparison with previously published SRR methods shows favorable characteristics in the proposed approach.

Blind image deconvolution through support vector regression.

This letter introduces a new algorithm for the restoration of a noisy blurred image based on the support vector regression (SVR). Experiments show that the performance of the SVR is very robust in blind image deconvolution where the types of blurs, point spread function (PSF) support, and noise level are all unknown.

Super-resolution reconstruction of hyperspectral images.

Hyperspectral images are used for aerial and space imagery applications, including target detection, tracking, agricultural, and natural resource exploration. Unfortunately, atmospheric scattering, secondary illumination, changing viewing angles, and sensor noise degrade the quality of these images. Improving their resolution has a high payoff, but applying super-resolution techniques separately to every spectral band is problematic for two main reasons. First, the number of spectral bands can be in the hundreds, which increases the computational load excessively. Second, considering the bands separately does not make use of the information that is present across them. Furthermore, separate band super-resolution does not make use of the inherent low dimensionality of the spectral data, which can effectively be used to improve the robustness against noise. In this paper, we introduce a novel super-resolution method for hyperspectral images. An integral part of our work is to model the hyperspectral image acquisition process. We propose a model that enables us to represent the hyperspectral observations from different wavelengths as weighted linear combinations of a small number of basis image planes. Then, a method for applying super resolution to hyperspectral images using this model is presented. The method fuses information from multiple observations and spectral bands to improve spatial resolution and reconstruct the spectrum of the observed scene as a combination of a small number of spectral basis functions.

Distributed video streaming using multiple description coding and unequal error protection.

This paper presents a distributed video streaming framework using unbalanced multiple description coding (MDC) and unequal error protection. In the proposed video streaming framework, two senders simultaneously stream complementary descriptions to a single receiver over different paths. To minimize the overall distortion and exploit the benefits of multipath transport when the characteristics of each path are different, an unbalanced MDC method for wavelet-based coders combined with a TCP-friendly rate allocation algorithm is proposed. The proposed rate allocation algorithm adjusts the transmission rates and the channel coding rates for all senders in a coordinated fashion to minimize the overall distortion. Simulation results show that the proposed unbalanced MDC combined with our rate allocation algorithm achieves about 1-6 dB higher peal signal-to-noise ratio compared to conventional balanced MDC when the available bandwidths along the two paths are different under time-varying network conditions.

A multiple-substream unequal error-protection and error-concealment algorithm for SPIHT-coded video bitstreams.

This paper presents a coordinated multiple-substream unequal error-protection and error-concealment algorithm for SPIHT-coded bitstreams transmitted over lossy channels. In the proposed scheme, we divide the video sequence corresponding to a group of pictures into two subsequences and independently encode each subsequence using a three-dimensional SPIHT algorithm. We use two different partitioning schemes to generate the substreams, each of which offers some advantages under the appropriate channel condition. Each substream is protected by an FEC-based unequal error-protection algorithm, which assigns unequal forward error correction codes to each bit plane. Any information that is lost during the transmission for any substream is estimated at the receiver by using the correlation between the substreams and the smoothness of the video signal. Simulation results show that the proposed multiple-substream UEP algorithm is simple, fast, and robust in hostile network conditions, and that the proposed error-concealment algorithm can achieve 2-3-dB PSNR gain over the case when error concealment is not used at high packet-loss rates.

Improved structures of maximally decimated directional filter banks for spatial image analysis.

This paper introduces an improved structure for directional filter banks (DFBs) that preserves the visual information in the subband domain. The new structure achieves this outcome while preserving both the efficient polyphase implementation and the exact reconstruction property. The paper outlines a step-by-step framework in which to examine the DFB, and within this framework discusses how, through the insertion of post-sampling matrices, visual distortions can be removed. In addition to the efficient tree structure, attention is given to the form and design of efficient linear phase filters. Most notably, linear phase IIR prototype filters are presented, together with the design details. These filters can enable the DFB to have more than a three-fold improvement in complexity reduction over quadrature mirror filters (QMFs).

Super-resolution reconstruction of compressed video using transform-domain statistics.

Considerable attention has been directed to the problem of producing high-resolution video and still images from multiple low-resolution images. This multiframe reconstruction, also known as super-resolution reconstruction, is beginning to be applied to compressed video. Super-resolution techniques that have been designed for raw (i.e., uncompressed) video may not be effective when applied to compressed video because they do not incorporate the compression process into their models. The compression process introduces quantization error, which is the dominant source of error in some cases. In this paper, we propose a stochastic framework where quantization information as well as other statistical information about additive noise and image prior can be utilized effectively.

Error-resilient image and video transmission over the Internet using unequal error protection.

This paper presents a new bit-plane-wise unequal error protection algorithm for progressive bitstreams transmitted over lossy networks. The proposed algorithm protects a compressed embedded bitstream generated by a 3-D SPIHT algorithm by assigning an unequal amount of forward error correction (FEC) to each bit-plane. The proposed algorithm reduces the amount of side information needed to send the size of each code to the decoder by limiting the number of quality levels to the number of bit-planes to be sent while providing a graceful degradation of picture quality as packet losses increase. We also apply our proposed algorithm to transmission of JPEG 2000 coded images over the Internet. To get additional error-resilience at high packet loss rates, we extend our algorithm to multiple-substream unequal error protection. Simulation results show that the proposed algorithm is simple, fast and robust in hostile network conditions and, therefore, can provide reasonable picture quality for video applications under varying network conditions.

A fast parametric motion estimation algorithm with illumination and lens distortion correction.

Methods for estimating motion in video sequences that are based on the optical flow equation (OFE) assume that the scene illumination is uniform and that the imaging optics are ideal. When these assumptions are appropriate, these methods can be very accurate, but when they are not, the accuracy of the motion field drops off accordingly. This paper extends the models upon which the OFE methods are based to include irregular, time-varying illumination models and models for imperfect optics that introduce vignetting, gamma, and geometric warping, such as are likely to be found with inexpensive PC cameras. The resulting optimization framework estimates the motion parameters, illumination parameters, and camera parameters simultaneously. In some cases these models can lead to nonlinear equations which must be solved iteratively; in other cases, the resulting optimization problem is linear. For the former case an efficient, hierarchical, iterative framework is provided that can be used to implement the motion estimator.

Eigenface-domain super-resolution for face recognition.

Face images that are captured by surveillance cameras usually have a very low resolution, which significantly limits the performance of face recognition systems. In the past, super-resolution techniques have been proposed to increase the resolution by combining information from multiple images. These techniques use super-resolution as a preprocessing step to obtain a high-resolution image that is later passed to a face recognition system. Considering that most state-of-the-art face recognition systems use an initial dimensionality reduction method, we propose to transfer the super-resolution reconstruction from pixel domain to a lower dimensional face space. Such an approach has the advantage of a significant decrease in the computational complexity of the super-resolution reconstruction. The reconstruction algorithm no longer tries to obtain a visually improved high-quality image, but instead constructs the information required by the recognition system directly in the low dimensional domain without any unnecessary overhead. In addition, we show that face-space super-resolution is more robust to registration errors and noise than pixel-domain super-resolution because of the addition of model-based constraints.

Multiframe error concealment for MPEG-coded video delivery over error-prone networks.

Compressed video sequences are very vulnerable to channel disturbances when they are transmitted through an unreliable medium such as a wireless channel. Transmission errors not only corrupt the current decoded frame, but they may also propagate to succeeding frames. A number of post-processing error concealment (ECN) methods that exploit the spatial and/or temporal redundancy in the video signal have been proposed to combat channel disturbances. Although these approaches can effectively conceal lost or erroneous macroblocks (MBs), all of them only consider spatial and/or temporal correlation in a single frame (the corrupted one), which limits their ability to obtain an optimal recovery. Since the error propagates to the next few motion-compensated frames in the presence of lost MBs in an I or P frame, error concealment should simultaneously minimize the errors not only in the current decoded frame but also in the succeeding B and P frames that depend on the corrupted frame. We propose a novel multiframe recovery principle which analyzes the propagation of a lost MB into succeeding frames. Then, MPEG-compatible spatial and temporal error concealment approaches using this multiframe recovery principle are proposed, where the lost MBs are recovered in such a way that the error propagation is minimized.

Color plane interpolation using alternating projections.

Most commercial digital cameras use color filter arrays to sample red, green, and blue colors according to a specific pattern. At the location of each pixel only one color sample is taken, and the values of the other colors must be interpolated using neighboring samples. This color plane interpolation is known as demosaicing; it is one of the important tasks in a digital camera pipeline. If demosaicing is not performed appropriately, images suffer from highly visible color artifacts. In this paper we present a new demosaicing technique that uses inter-channel correlation effectively in an alternating-projections scheme. We have compared this technique with six state-of-the-art demosaicing techniques, and it outperforms all of them, both visually and in terms of mean square error.