Super-resolution reconstruction based on linear interpolation of wavelet coefficients

High resolution image reconstruction is an image process to reconstruct a high resolution image from a set of blurred, degraded and shifted low resolution images. In this paper, the reconstruction problem is treated as a function approximation. We use linear interpolation to build up an algorithm to obtain the relationship between the detail coefficients in wavelet subbands and the set of low resolution images. We use Haar wavelet as an example and establish the connection between the Haar wavelet subband and the low resolution images. Experiments show that we can use just 3 low resolution images to obtain a high resolution image which has better quality than Tikhonov least-squares approach and Chan et al. Algorithm 3 in low noise cases. We also propose an error correction extension for our method which can lead to very good results even in noisy cases. Moreover, our approach is very simple to implement and very efficient.     

Multiresolution Gauss-Markov random field models for texturesegmentation

This paper presents multiresolution models for Gauss-Markov random fields (GMRFs) with applications to texture segmentation. Coarser resolution sample fields are obtained by subsampling the sample field at fine resolution. Although the Markov property is lost under such resolution transformation, coarse resolution non-Markov random fields can be effectively approximated by Markov fields. We present two techniques to estimate the GMRF parameters at coarser resolutions from the fine resolution parameters, one by minimizing the Kullback-Leibler distance and another based on local conditional distribution invariance. We also allude to the fact that different GMRF parameters at the fine resolution can result in the same probability measure after subsampling and present the results for first- and second-order cases. We apply this multiresolution model to texture segmentation. Different texture regions in an image are modeled by GMRFs and the associated parameters are assumed to be known. Parameters at lower resolutions are estimated from the fine resolution parameters. The coarsest resolution data is first segmented and the segmentation results are propagated upward to the finer resolution. We use the iterated conditional mode (ICM) minimization at all resolutions. Our experiments with synthetic, Brodatz texture, and real satellite images show that the multiresolution technique results in a better segmentation and requires lesser computation than the single resolution algorithm.     

Projection x-space magnetic particle imaging

Projection magnetic particle imaging (MPI) can improve imaging speed by over 100-fold over traditional 3-D MPI. In this work, we derive the 2-D x-space signal equation, 2-D image equation, and introduce the concept of signal fading and resolution loss for a projection MPI imager. We then describe the design and construction of an x-space projection MPI scanner with a field gradient of 2.35 T/m across a 10 cm magnet free bore. The system has an expected resolution of 3.5 × 8.0 mm using Resovist tracer, and an experimental resolution of 3.8 × 8.4 mm resolution. The system images 2.5 cm × 5.0 cm partial field-of views (FOVs) at 10 frames/s, and acquires a full field-of-view of 10 cm × 5.0 cm in 4 s. We conclude by imaging a resolution phantom, a complex "Cal" phantom, mice injected with Resovist tracer, and experimentally confirm the theoretically predicted x-space spatial resolution.     

Compensation for nonuniform resolution using penalized-likelihood reconstruction in space-variant imaging systems

Imaging systems that form estimates using a statistical approach generally yield images with nonuniform resolution properties. That is, the reconstructed images possess resolution properties marked by space-variant and/or anisotropic responses. We have previously developed a space-variant penalty for penalized-likelihood (PL) reconstruction that yields nearly uniform resolution properties. We demonstrated how to calculate this penalty efficiently and apply it to an idealized positron emission tomography (PET) system whose geometric response is space-invariant. In this paper, we demonstrate the efficient calculation and application of this penalty to space-variant systems. (The method is most appropriate when the system matrix has been precalculated.) We apply the penalty to a large field of view PET system where crystal penetration effects make the geometric response space-variant, and to a two-dimensional single photon emission computed tomography system whose detector responses are modeled by a depth-dependent Gaussian with linearly varying full-width at half-maximum. We perform a simulation study comparing reconstructions using our proposed PL approach with other reconstruction methods and demonstrate the relative resolution uniformity, and discuss tradeoffs among estimators that yield nearly uniform resolution. We observe similar noise performance for the PL and post-smoothed maximum-likelihood (ML) approaches with carefully matched resolution, so choosing one estimator over another should be made on other factors like computational complexity and convergence rates of the iterative reconstruction. Additionally, because the postsmoothed ML and the proposed PL approach can outperform one another in terms of resolution uniformity depending on the desired reconstruction resolution, we present and discuss a hybrid approach adopting both a penalty and post-smoothing.     

Coherent optical frequency domain reflectometry usingphase-decorrelated reflected and reference lightwaves

We report a new scheme for coherent optical frequency domain reflectometry (C-OFDR) where the coherence length of the lightwaves does not limit the measureable fiber length. In this scheme, we use the beat spectrum which results when we mix reflected and reference lightwaves whose phases are not correlated. We demonstrated this scheme using a narrow-linewidth-lightwave source and an external electro-optical phase modulator. We measured Rayleigh backscattering and Fresnel reflections from a 30-Ion optical fiber, and achieved a spatial resolution of 5 m for two neighboring Fresnel reflectors located at the far end of the fiber. We estimated the expected spatial resolution and single-way dynamic range for our new scheme and show that it is capable of measuring long optical fibers with high-spatial resolution     

Wavefield modeling and array processing. III. Resolution capacity

For pt.II see ibid., vol.42, no.10, p.2560, 1994. We use here the wavefield modeling formalism in order to derive some fundamental limitations on the performance of passive arrays. We first derive upper bounds on both the ideal and effective resolution capacities, and show that the effective resolution capacity is bounded by the effective rank of the relevant sampling matrix, i.e., is determined by the array structure. We then study how the resolution capacity is affected when the sources are constrained to lie within an angular sector. We also look at the case where the source spectra are known a priori to be flat, and show that, contrary to the common opinion, if the array satisfies the spectral sampling condition, the effective wideband resolution capacity is bounded by the same bound as the narrowband one. We conclude by examining the interesting phenomenon of resonance in arrays. We show that these resonances, far from being mathematical artifacts, can manifest quite dramatically in the performance of arrays     

Single frame image super-resolution: should we process locally or globally?

In this paper we study the usefulness of different local and global, learning-based, single-frame image super-resolution reconstruction techniques in handling three specific tasks, namely, de-blurring, de-noising and alias removal. We start with the global, iterative Papoulis–Gerchberg method for super-resolving a scene. Next we describe a PCA-based global method which faithfully reproduces a super-resolved image from a blurred and noisy low resolution input. We also study several multi-resolution processing schemes for super-resolution where the best edges are learned locally from an image database. We show that the PCA-based global method is efficient in handling blur and noise in the data. The local methods are adept in capturing the edges properly. However, both local and global approaches cannot properly handle the aliasing present in the low resolution observation. Hence we propose an alias removal technique by designing an alias-free upsampling scheme. Here the unknown high frequency components of the given partially aliased (low resolution) image is generated by minimizing the total variation of the interpolant subject to the constraint that part of alias free spectral components in the low resolution observation are known precisely and under the assumption of sparsity in the data.     

超高分辨率机载SAR 宽带激励源设计与实现

针对分辨率优于0.1 m 的机载合成孔径雷达(SAR)系统,该文设计实现了中心频率14.8 GHz,带宽3.2 GHz的宽带线性调频(LFM)激励信号源。详细介绍了技术方案的选择,关键技术的实现,并对产生的宽带调频信号进行了详细的测试与分析。该信号源作为机载SAR 系统中子系统的一部分,完成了飞行试验,并获得了分辨率优于0.1m 的雷达图像,验证了该方案设计和技术实现的有效性。      

A modified uniform Cramer-Rao bound for multiple pinhole aperture design

This paper presents a modified Uniform Cramer-Rao bound (UCRB) for studying estimator spatial resolution and variance tradeoffs. We proposed to use a resolution constraint that is imposed on mean gradient vectors of achieved estimators and derived the minimum achievable variance for any estimator satisfies this resolution constraint. This approach partially overcomes the limitations of the former UCRB approach based on a bias-gradient norm constraint. We applied this method in a feasibility study of using multiple pinhole apertures for small animal SPECT imaging applications. The SPECT system studied was based on an existing gamma camera. The achievable spatial resolution and variance tradeoffs for systems with different design parameters, such as number of pinholes and pinhole size, were studied.     

Fiber-Optic Distributed Strain and Temperature Sensing With Very High Measurand Resolution Over Long Range Using Coherent OTDR

We describe a novel fiber-optic technique for measuring distributed strain and temperature that uses a coherent optical time-domain reflectometer (OTDR) with a precisely frequency-controlled light source. Using this technique, we achieved temperature measurements in an 8-km-long fiber with a resolution of 0.01degC and a spatial resolution of one meter. This temperature resolution is two orders of magnitude better than that provided by the Brillouin based sensing technique.     

A network for large numbers of interferometric sensors and fiberBragg gratings with high resolution and extended range

We describe an active form of a frequency division matrix array network developed to support both interferometric sensors and fiber Bragg gratings to demodulate both quasistatic and periodic measurands. Novel telemetry allows an improved measurement range using a single source and uses a fundamental interferometric principle for self-calibration. High resolution is achieved through active but remote demodulation of passive sensors. A resolution of 0.019 nm with a measurement range of 62.8 μm for an interferometric sensor and a strain resolution of 0.19 με with a measurement range of 6.28 mε was obtained for fiber Bragg grating sensors     

On the Design of Optimal Policy for Sharing Finite Buffers

An important analytic model of finite buffer systems is a multiclass single queue with typed servers. In the context of such a model, we address the problem of selecting an optimal buffer sharing policy. We show that with respect to weighted throughput, an optimal policy must be a stationary delayed resolution policy. An iterative procedure based upon the policy iteration method for Markov processes with rewards is used to efficiently search for the optimal delayed resolution policy for a given set of system parameters. A performance comparison of the optimal delayed resolution policy with other well-known buffer sharing policies is also provided.     

The fusion of different resolution SAR images

We explore the possibility of using different data sets relative to the same scene to obtain a better knowledge of the scene than the one obtained using only one data set. In particular we concentrate on the fusion of two different spatial resolution images, although the method we propose can be regarded as a method of more general interest. The fused image has the least mean square deviation from the finer resolution image, subject to the constraints imposed by the knowledge of the coarser resolution image. Fusion is obtained by solving a constrained quadratic highly parallelizable minimization problem. Explicit formulas for the solution of the minimization problem are given. The number of elementary operations required is proportional to the number of pixels of the finer resolution image. We test our method on a class of simulated images that reproduce some features of synthetic aperture radar (SAR) images. As an example we consider the problem of detection of point scatterers in a uniform background. The results obtained show that the information from the coarser resolution image can significantly improve the quality of the reconstructed scene obtained from the finer resolution image     

Down-scaling for better transform compression

The most popular lossy image compression method used on the Internet is the JPEG standard. JPEG's good compression performance and low computational and memory complexity make it an attractive method for natural image compression. Nevertheless, as we go to low bit rates that imply lower quality, JPEG introduces disturbing artifacts. It is known that, at low bit rates, a down-sampled image, when JPEG compressed, visually beats the high resolution image compressed via JPEG to be represented by the same number of bits. Motivated by this idea, we show how down-sampling an image to a low resolution, then using JPEG at the lower resolution, and subsequently interpolating the result to the original resolution can improve the overall PSNR performance of the compression process. We give an analytical model and a numerical analysis of the down-sampling, compression and up-sampling process, that makes explicit the possible quality/compression trade-offs. We show that the image auto-correlation can provide a good estimate for establishing the down-sampling factor that achieves optimal performance. Given a specific budget of bits, we determine the down-sampling factor necessary to get the best possible recovered image in terms of PSNR.     

Comparison of thermoreflectance and scanning thermal microscopy for microelectronic device temperature variation imaging: Calibration and resolution issues

We have studied temperature variations on two submicrometric dissipative structures with two different techniques. On one hand, we have used a thermoreflectance imaging technique which is a well-known non contact optical method to evaluate temperature variations but whose spatial resolution is limited by diffraction. On the other hand, we have used a scanning thermal microscope (SThM) to study the thermal behaviour of these small dissipative structures. We present qualitative results obtained by both methods and we compare their advantages and drawbacks in terms of calibration and spatial resolution for thermal measurements on microelectronic devices. In particular, we show how the thermoreflectance coefficient can become an advantage to enhance the image contrast and favour the spatial resolution.     

Resolution limits in signal recovery

Resolution analysis for the problem of signal recovery from finitely many linear measurements is the subject of this paper. The classical Rayleigh limit serves only as a lower bound on resolution since it does not assume any recovery strategy and is based only on observed data. We show that details finer than the Rayleigh limit can be recovered by simple linear processing that incorporates prior information. We first define a measure of resolution based on allowable levels of error that is more appropriate for current signal recovery strategies than the Rayleigh definition. In the practical situation in which only finitely many noisy observations are available, we have to restrict the class of signals in order to make the resolution measure meaningful. We consider the set of bandlimited and essentially timelimited signals since it describes most signals encountered in practice. For this set, we show how to precompute resolution limits from knowledge of measurement functionals, signal-to-noise ratio, passband, energy concentration regions, energy concentration factor, and a prescribed level of error tolerance. In the process, we also derive an algorithm for high-resolution signal recovery. We illustrate the results with examples in one and two dimensions     

Temporal resolution of an AlxGa1?xAs/GaAs bias-free photodetector

We report on the measurement of the temporal resolution limit of a recently reported AlxGa1?xAs/GaAs bias-free photodetector. When tested by dual synchronously pumped dye laser pulses with a repetition rate of 80 MHz and an attenuated energy per pulse of 30 pJ, the detector shows a resolution limit of ? 65 ps.     

A study of deadlock models for a multiservice medium accessprotocol employing a slotted Aloha signalling channel

Medium access protocols for HFC and wireless ATM networks often use a collision based capacity request signalling channel which may rely on the slotted Aloha multiaccess principle. This paper studies the performance of a p-persistence slotted Aloha contention resolution algorithm (CRA), subject to extreme interstation correlation, by means of a discrete-time Markov chain analysis. We examine in detail the conditions leading to a deadlock-a situation where the time to collision resolution becomes unacceptably high and the system is practically unstable. We analyze two disaster scenario deadlock models, and study the effect of channel error probability, signalling traffic load, and the contention resolution algorithm used. We show that the key factor of the CRA is the collision rate and not channel errors. We propose and test three signalling channel capacity allocation schemes. We identify the best-performing of these three schemes as the cyclic contention mini-slot (CMS) sharing employing multiple CMSs per data slot. Finally, we demonstrate the need for implementation of an added scheme, which dynamically adjusts the p-persistence parameter     

Nonparametric regression sinogram smoothing using a roughness-penalized Poisson likelihood objective function

We develop and investigate an approach to tomographic image reconstruction in which nonparametric regression using a roughness-penalized Poisson likelihood objective function is used to smooth each projection independently prior to reconstruction by unapodized filtered backprojection (FBP). As an added generalization, the roughness penalty is expressed in terms of a monotonic transform, known as the link function, of the projections. The approach is compared to shift-invariant projection filtering through the use of a Hanning window as well as to a related nonparametric regression approach that makes use of an objective function based on weighted least squares (WLS) rather than the Poisson likelihood. The approach is found to lead to improvements in resolution-noise tradeoffs over the Hanning filter as well as over the WLS approach. We also investigate the resolution and noise effects of three different link functions: the identity, square root, and logarithm links. The choice of link function is found to influence the resolution uniformity and isotropy properties of the reconstructed images. In particular, in the case of an idealized imaging system with intrinsically uniform and isotropic resolution, the choice of a square root link function yields the desirable outcome of essentially uniform and isotropic resolution in reconstructed images, with noise performance still superior to that of the Hanning filter as well as that of the WLS approach.