nils-herrmann/clean_scirepeval huggingface dataset:
A compressive sensing approach of multiple descriptions for network multimedia communication

A new multiple description coding (MDC) approach is proposed based on the theory of compressive sensing (CS). The CS theory allows a signal to be reconstructed from a small number of its random measurements if the signal is sparse in some space. An attractive property of CS for MDC applications is that the reconstruction error only depends on the number but not on which of the transmitted measurements that are received. By treating each CS measurement as a description, we have a balanced MDC scheme with fine description granularity and low encoding complexity. Another advantage of the new MDC approach is that all signals can be coded the same but decoded in different spaces for better sparse reconstruction.
Compressive sensing based image transmission with side information at the decoder

This paper proposes a distributed compressive sensing (CS) scheme for robust image transmission over unknown or time-varying channels with highly correlated images at the decoder. A compressed thumbnail is first transmitted after digital forward error correction (FEC) and modulation to retrieve highly correlated images and generate a side information (SI) at the decoder. The current residual image after subtracting the decompressed thumbnail is then coded and transmitted by CS through a very dense constellation without FEC. The linear representation of the residual signal by CS measurements and rateless sampling makes it able to achieve graceful degradation and bandwidth scalability without channel feedback. Moreover, a transform-domain power allocation is employed before random sampling to protect against channel errors. At the decoder, both the nonlocal correlations within the original image and the correlation with the SI are exploited in CS decoding via a low-rank regulation on similar patches. After CS decoding, a block-wise minimum-mean-square-error (MMSE) reconstruction using the SI is further performed in the spatial domain to enhance the reconstruction quality. Simulations on landmark images and an unknown Gaussian channel show that an up to 10 dB gain is achieved at low channel SNRs compared with the state-of-the-art uncoded image transmission scheme, i.e. SoftCast, when highly correlated images are available at the decoder.
Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization

As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation owes to the development of the l1-norm optimization techniques and the fact that natural images are intrinsically sparse in some domains. The image restoration quality largely depends on whether the employed sparse domain can represent well the underlying image. Considering that the contents can vary significantly across different images or different patches in a single image, we propose to learn various sets of bases from a precollected dataset of example image patches, and then, for a given patch to be processed, one set of bases are adaptively selected to characterize the local sparse domain. We further introduce two adaptive regularization terms into the sparse representation framework. First, a set of autoregressive (AR) models are learned from the dataset of example image patches. The best fitted AR models to a given patch are adaptively selected to regularize the image local structures. Second, the image nonlocal self-similarity is introduced as another regularization term. In addition, the sparsity regularization parameter is adaptively estimated for better image restoration performance. Extensive experiments on image deblurring and super-resolution validate that by using adaptive sparse domain selection and adaptive regularization, the proposed method achieves much better results than many state-of-the-art algorithms in terms of both PSNR and visual perception.
ISAR imaging with separable sparse representation model

It is well-known that the sparse representation (SR) based ISAR imaging schemes suffer from high cost on memory and computation complexity, because the two-dimensional imaging observation procedure is jointly represented by a large-scale basis matrix. To overcome this difficulty, a novel separable SR-based ISAR imaging algorithm is developed in this paper. By introducing a separable observation model, the large-scale basis is replaced by two small-scale bases, which brings significant reduction on memory and complexity. To efficiently solve the imaging problem, the range profile is engaged as an auxiliary variable, and the sparsity on both the scene and the range profile is exploited, by which the scene can be reconstructed in matrix operation directly instead of the memory consuming vector operations. Compared with the traditional SR-based imaging scheme, the memory cost for storing the basis matrix can be dropped from MN×PQ to MP+QN, and the computation complexity is reduced from O(P3 Q3) to O(P3+Q3), where M, P and N, Q stand for the numbers of samples and grids in range and cross-range directions. The superior performance of the proposed algorithm is proved by simulated data and real data.
Bandwidth adaption for kernel particle filter

A novel particle filter bandwidth adaption for kernel particle filter (BAKPF) is proposed. Selection of the kernel bandwidth is a critical issue in kernel density estimation (KDE). The plug-in method is adopted to get the global fixed bandwidth byoptimizing the asymptotic mean integrated squared error (AMISE) firstly. Then, particle-driven bandwidth selection is invoked in the KDE. To get a more effective allocation of the particles, the KDE with adaptive bandwidth in the BAKPF is used to approximate the posterior probability density function (PDF) by moving particles toward the posterior. A closed-form expression of the true distribution is given. The simulation results show that the proposed BAKPF performs better than the standard particle filter (PF), unscented particle filter (UPF) and the kernel particle filter (KPF) both in efficiency and estimation precision.
Model-Assisted Adaptive Recovery of Compressed Sensing with Imaging Applications

In compressive sensing (CS), a challenge is to find a space in which the signal is sparse and, hence, faithfully recoverable. Since many natural signals such as images have locally varying statistics, the sparse space varies in time/spatial domain. As such, CS recovery should be conducted in locally adaptive signal-dependent spaces to counter the fact that the CS measurements are global and irrespective of signal structures. On the contrary, existing CS reconstruction methods use a fixed set of bases (e.g., wavelets, DCT, and gradient spaces) for the entirety of a signal. To rectify this problem, we propose a new framework for model-guided adaptive recovery of compressive sensing (MARX) and show how a 2-D piecewise autoregressive model can be integrated into the MARX framework to make CS recovery adaptive to spatially varying second order statistics of an image. In addition, MARX offers a mechanism of characterizing and exploiting structured sparsities of natural images, greatly restricting the CS solution space. Simulation results over a wide range of natural images show that the proposed MARX technique can improve the reconstruction quality of existing CS methods by 2-7 dB.
Image restoration via Bayesian structured sparse coding

In this work, we propose a Bayesian structured sparse coding (BSSC) framework containing a nonlocal extension of Gaussian scale mixture (GSM) model by exploiting structured sparsity. It is shown that the variances of sparse coefficients (the field of Gaussian scalars) - if treated as a latent variable - can besparse coefficients jointly estimated along with the unknown sparse coefficients via the the method of alternative optimization. When applied to image restoration, BSSC leads to closed-form solutions involving iterative shrinkage/filtering and therefore admits computationally efficient implementation. Our experimental results have shown that BSSC-based image restoration often delivers reconstructed images with higher subjective/objective qualities than other competing approaches including IDD-BM3D and NCSR.
Reduced-reference image quality assessment with local binary structural pattern

Reduced-reference (RR) image quality assessment (IQA) aims to use less reference data and achieve higher quality prediction accuracy. Recent researches confirm that the human visual system (HVS) is adapted to extract structural information and is sensitive to structure degradation. Therefore, in this paper, we try to represent image contents with several structural patterns, and measure image quality according to the structural degradation on these patterns. The classic local binary patterns (LBPs) are firstly employed to extract image structures and create LBP based structural histogram. And then, the structural degradation is computed as the histogram distance between the reference and distorted images. Experimental results on three large databases demonstrate that the proposed RR IQA method greatly improved the quality prediction accuracy.
Adaptive wavelet thresholding for time varying SNR signal denoising

The wavelet transform has become a powerful tool for signal analysis and is widely used in many applications, including signal detection and denoising. This paper deals with signal detection and denoising using wavelet transforms for a class of time varying signal-to-noise-ratio (SNR) signals in B-type ultrasonic echoes. A new threshold estimator is proposed in order to enhance the SNR in time varying signals. With the estimator used on real B-type ultrasonic echo data, the results show that the proposed estimator is superior to the existing threshold estimator in SNR enhancement.
An Improved Signed Digit Representation Approach for Constant Vector Multiplication

In this brief, the multiplier-free implementation of the constant vector multiplication is reexamined. A novel improved signed digit representation technique is proposed to overcome the two main drawbacks of the current multiplier-free techniques: 1) computational redundancy and 2) circuit irregularity. The fundamental difference between the proposed technique and the existing multiplier-free techniques is a novel optimization framework based on vector decomposition. The constant vector is decomposed into two terms: a “public” vector and a “private” matrix which consist of the public operations shared by all of the entries and the private operations of each individual entry, respectively. In this way, the overall data flow can be divided into two regular steps: multiplied by the “public” vector first and then by the “private” matrix. The computational complexity reduction task is then achieved by minimizing the length of the “public” vector and the number of operations in the “private” matrix. Experimental results demonstrate that the proposed technique outperforms the existing multiplier-free techniques in fewer operations and more regular circuit structure.
Directional wavelet transform and block-set partitioning for image coding

We propose a novel directional lifting-based block-set coding algorithm in this paper. Directional lifting wavelet transform based on region partition is firstly used to remove the correlation along the edges and reduce the amount of high-pass subband energy, then a new multi-symbol arithmetic coding based on residual classified context is exploited to code directional information. A context-based block-set partitioning method is proposed to code the directional transformed coefficients. Experimental results show that the proposed algorithm for texture-rich images outperforms JPEG2000 by 1.6 dB. Especially, the new algorithm can obtain the PSNR gain which up to 0.4 dB, comparing with JPEG2000 (EBCOT) based on directional lifting transform.
High-speed hyperspectral video acquisition with a dual-camera architecture

We propose a novel dual-camera design to acquire 4D high-speed hyperspectral (HSHS) videos with high spatial and spectral resolution. Our work has two key technical contributions. First, we build a dual-camera system that simultaneously captures a panchromatic video at a high frame rate and a hyperspectral video at a low frame rate, which jointly provide reliable projections for the underlying HSHS video. Second, we exploit the panchromatic video to learn an over-complete 3D dictionary to represent each band-wise video sparsely, and a robust computational reconstruction is then employed to recover the HSHS video based on the joint videos and the self-learned dictionary. Experimental results demonstrate that, for the first time to our knowledge, the hyperspectral video frame rate reaches up to 100fps with decent quality, even when the incident light is not strong.
Image Enhancement by Entropy Maximization and Quantization Resolution Upconversion

This article introduces a new contrast enhancement algorithm of tone-preserving entropy maximization. Its design objective is to present the maximal amount of information content in the enhanced image, or being optimal in an information theoretical sense, while preventing the loss of tone continuity. The resulting optimization problem can be graph theoretically modeled as the construction of the $K$ -edges maximum-weight path, and it can be solved efficiently by dynamic programming. Moreover, the proposed algorithm is made more effective by being combined with a preprocess of image restoration that aims to correct quantization errors caused by the analog-to-digital conversion of image signals. Empirical evidences are provided to demonstrate the superior visual quality obtained by the new image enhancement algorithm.
High resoluton image reconstruction: A new imager via movable random exposure

Recently compressive sensor developed as an imager for capturing images effectively has been studied extensively. In this paper, we design a new imager to reconstruct high resolution image from a low resolution blurred image obtained by the intended movable random exposure. This imager grabs an image by moving a camera with a randomly fluttering shutter along a certain motion route. By analyzing this kind of movable random exposure process, we find it can be considered as compressive sampling described in the compressive sensing (CS) theory. Then according to the CS theory, the exposure result of this imager can be used to recover a high resolution image. Since this imager consists only a movable camera and a fluttered shutter, it is relatively simple and easy to implement. The simulation results show that the proposed imager can recover high even ultra-high resolution images with good reconstruction performance.
Energy Efficient Resource Allocation for Wireless Power Transfer Enabled Collaborative Mobile Clouds

In order to fully enjoy high rate broadband multimedia services, prolonging the battery lifetime of user equipment is critical for mobile users, especially for smartphone users. In this paper, the problem of distributing cellular data via a wireless power transfer enabled collaborative mobile cloud (WeCMC) in an energy efficient manner is investigated. WeCMC is formed by a group of users who have both functionalities of information decoding and energy harvesting, and are interested for cooperating in downloading content from the operators. Through device-to-device communications, the users inside WeCMC are able to cooperate during the downloading procedure and offload data from the base station to other WeCMC members. When considering multi-input multi-output wireless channel and wireless power transfer, an efficient algorithm is presented to optimally schedule the data offloading and radio resources in order to maximize energy efficiency as well as fairness among mobile users. Specifically, the proposed framework takes energy minimization and quality of service requirement into consideration. Performance evaluations demonstrate that a significant energy saving gain can be achieved by the proposed schemes.
Fast Hash-Based Inter-Block Matching for Screen Content Coding

In the latest High Efficiency Video Coding (HEVC) development, i.e., HEVC screen content coding extensions (HEVC-SCC), a hash-based inter-motion search/block matching scheme is adopted in the reference test model, which brings significant coding gains to code screen content. However, the hash table generation itself may take up to half the encoding time and is thus too complex for practical usage. In this paper, we propose a hierarchical hash design and the corresponding block matching scheme to significantly reduce the complexity of hash-based block matching. The hierarchical structure in the proposed scheme allows large block calculation to use the results of small blocks. Thus, we avoid redundant computation among blocks with different sizes, which greatly reduces complexity without compromising coding efficiency. The experimental results show that compared with the hash-based block matching scheme in the HEVC-SCC test model (SCM)-6.0, the proposed scheme reduces about 77% of hash processing time, which leads to 12% and 16% encoding time savings in random access (RA) and low-delay B coding structures. The proposed scheme has been adopted into the latest SCM. A parallel implementation of the proposed hash table generation on graphics processing unit (GPU) is also presented to show the high parallelism of the proposed scheme, which achieves more than 30 frames/s for 1080p sequences and 60 frames/s for 720p sequences. With the fast hash-based block matching integrated into x265 and the hash table generated on GPU, the encoder can achieve 11.8% and 14.0% coding gains on average for RA and low-delay P coding structures, respectively, for real-time encoding.
Sparsity fine tuning in wavelet domain with application to compressive image reconstruction

In compressive sensing, wavelet space is widely used to generate sparse signal (image signal in particular) representations. In this work, we propose a novel approach of statistical context modeling to increase the level of sparsity of wavelet image representations. It is shown, contrary to a widely held assumption, that high-frequency wavelet coefficients have non-zero mean distributions if conditioned on local image structures. Removing this bias can make wavelet image representations sparser, i.e., having a greater number of zero and close-to-zero coefficients. The resulting unbiased probability models can significantly improve the performance of existing wavelet-based compressive image reconstruction methods in both PSNR and visual quality.
Visual information measurement with quality assessment

The quantity of visual information measurement is significant for many perception-oriented signal processing system. The classical Shannon theory, which based on the probability of signal, is useful to measure the quantity of channel information. However, it fails to accurately measure the quantity of visual information of a given image. Image quality refers to the subjective perception on the visual information that an image carried. An image with high quality carries more information than that of a low quality image. Thus, the quality of an image can effectively represent its quantity of visual information. In this paper, we propose a novel visual information measurement, and verify it with quality assessment. Firstly, a dictionary is learned from natural images, in which the content change of each atom is calculated to present its quantity of visual information. Then, a testing image is represented by the dictionary, and the sparse coefficients for each local block in the image are acquired. Finally, according to the sparse coefficients, the quantity of visual information is measured. The information measurement result is verified with the subjective quality score. Experimental results on a large amount of images demonstrate the accuracy of the proposed method for quantity of visual information measurement.
High quality impulse noise removal via non-uniform sampling and autoregressive modelling based super-resolution

The challenge of image impulse noise removal is to restore spatial details from damaged pixels using remaining ones in random locations. Most existing methods use all uncontaminated pixels within a local window to estimate the centred noisy one via a statistic way. These kinds of methods have two defects. First, all noisy pixels are treated as independent individuals and estimated by their neighbours one by one, with the correlation between their true values ignored. Second, the image structure as a natural feature is usually ignored. This study proposes a new denoising framework, in which all noisy pixels are jointly restored via non-uniform sampling and supervised piecewise autoregressive modelling based super-resolution. In this method, the noisy pixels are jointly estimated in groups through solving a well-designed optimisation problem, in which image structure feature is considered as an important constraint. Another contribution is that piecewise autoregressive model is not simply adopted but carefully designed so that all noise-free pixels can be used to supervise the model training and optimisation problem solving for higher accuracy. The experimental results demonstrate that the proposed method exhibits good denoising performance in a large noise density range (10–90%).
SAR Imaging With Structural Sparse Representation

Sparse representation (SR)-based SAR imaging approaches have shown their superior performance compared with conventional approaches. However, for an image with rich spatial structures, a fixed global dictionary is usually ineffective to characterize the local structures. Piecewise autoregressive (PAR) model indicates that each pixel can be linearly represented by its local neighboring pixels. Inspired by this, an adaptive sparse space, effectively characterizing the varying image local structures, is designed, in which the entries are derived from the PAR model. By incorporating the adaptive SR into the SAR imaging, a novel structural SR-based SAR (SSR-SAR) imaging approach is proposed. Due to the fact that the adaptive sparse space is greatly dependent on the prior information of the SAR image, updating of the adaptive sparse space and SAR imaging is a joint optimization problem. In our approach, we propose to introduce the alternative minimization scheme to solve the problem. Besides, the Augmented Lagrangian Multiplier technique is adopted to accelerate the computation speed. Finally, experimental results are shown to demonstrate the validity of the proposed approach.
Design of Linear-Phase Nonuniform Filter Banks With Partial Cosine Modulation

The majority of the existing work on designing nonuniform filter banks (NUFBs) cannot achieve linear-phase (LP) property, because of the high complexity associated with phase distribution. This correspondence proposes an idea of partial cosine modulation to obtain the LP property of NUFBs with rational sampling factors. It makes the efficient modulation technique possible to be used to design LP NUFBs. Except the separately designed lowpass and highpass analysis/synthesis filters, we obtain the bandpass by cosine modulation of several prototypes, worth of the name ¿partial cosine modulation. By analyzing the phase issue of significant aliasing terms, we derive the matching conditions to make the required LP NUFB achievable. With these criteria being satisfied, the design problem becomes that of several prototypes as well as the lowpass and highpass filters, leading to a less design effort. By using the proposed method, near-perfect-reconstruction LP NUFBs can be obtained in a simple and efficient way as demonstrated by examples.