nils-herrmann/clean_scirepeval huggingface dataset:
UWB Echo Signal Detection With Ultra-Low Rate Sampling Based on Compressed Sensing

A major challenge in ultra-wide-band (UWB) signal processing is the requirement for very high sampling rate. The recently emerging compressed sensing (CS) theory makes processing UWB signal at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on the CS theory, a system for sampling UWB echo signal at a rate much lower than Nyquist rate and performing signal detection is proposed in this paper. First, an approach of constructing basis functions according to matching rules is proposed to achieve sparse signal representation because the sparse representation of signal is the most important precondition for the use of CS theory. Second, based on the matching basis functions and using analog-to-information converter, a UWB signal detection system is designed in the framework of the CS theory. With this system, a UWB signal, such as a linear frequency-modulated signal in radar system, can be sampled at about 10% of Nyquist rate, but still can be reconstructed and detected with overwhelming probability. The simulation results show that the proposed method is effective for sampling and detecting UWB signal directly even without a very high-frequency analog-to-digital converter.
Signal matching wavelet for ultrasonic flaw detection in high background noise

The wavelet transform (WT) is widely used in ultrasonic flaw detection (UFD) systems because of its property of multiresolution time-frequency analysis. Those traditional WT-based methods for UFD use a wavelet basis with limited types to match various echo signals (called wavelet matching signals), so it is difficult for those methods to achieve the optimal match between echo signal and wavelet basis. This results in limited detection ability in high background noise for those WT-based methods. In this paper, we propose a signal matching wavelet (SMW) method for UFD to solve this problem. Unlike traditional UFD systems, in the proposed SMW, the transmitted signal is designed to be a wavelet function for matching a wavelet basis. This makes it possible to obtain the optimal match between the echo signal and the wavelet basis. To achieve the optimal match from the aspect of energy, we derive three rules for designing the transmitted signal and selecting the wavelet basis. Further, the parameter selection in applying the proposed SMW method to a practical UFD system is analyzed. In addition, a low-rate discrete WT structure is designed to decrease the hardware cost, which facilitates the practical application of the proposed SMW. The simulation results show that the proposed SMW can efficiently detect flaws in high background noise even with SNR lower than -20 dB, outperforming the existing methods by 5 dB.
High resolution SAR imaging with efficient azimuth compression in chirp scaling principle

Chirp scaling algorithm (CSA) is one of the most popular algorithms in radar imaging, due to its excellent focusing ability and implementation simplicity. However, such superior performance, especially the resolution capability is greatly restricted by the number of observed measurements. Specially, if the measurements are reduced, images with high sidelobes and lower resolution are formed. The recent developed compressed sensing (CS) as well as its application in radar imaging demonstrates that, if the sparsity constraint is cooperated in the imaging technique, high resolution imaging quality can be still achieved, even using limited measurements. In this paper, a new chirp scaling azimuth compression technique based on CS theory is proposed, which exploits the sparse property of the measurements in azimuth dimension, and the sparsity constraint is combined with the CSA to perform all the azimuth processing steps. Comparisons were performed between the proposed algorithm using data under-sampled in the azimuth dimension at different ratios and the traditional CSA using full data set at different signal-to-noise ratio (SNR). Results show that the proposed CS based algorithm has better performance than the traditional algorithm even with using low percentage of the azimuth data and also indicate that the proposed algorithm is robust with the existence of low SNR.
Semi-automatic image registration using harris corner detection and RANdom SAmple Consensus (RANSAC)

Image registration is a fundamental image processing task to match and align physically two images which could have been imaged by different sensors, view angles and or at different times. This paper presents a new algorithm for image registration based on a combination between cross correlation function and feature-based methods .In the proposed algorithm, the ground control points are extracted from input images using the Harris corner detector, and the correspondence between the points extracted from the different images is established using RANdom SAmple Consensus (RANSAC) method with. The image coregistration in this paper is performed by three ways: It is first performed by the proposed algorithm then it is performed by feature-based methods and finally the results are compared with those achieved using Gamma design software. The method is also evaluated by using the coherence map. The results showed that the combination between cross correlation function and feature-based methods almost give us the same results of traditional method but with low processing time., which is our aim to prove and validate. (4 pages)
Progressive Quantization of Compressive Sensing Measurements

Compressive sensing (CS) is recently and enthusiastically promoted as a joint sampling and compression approach. The advantages of CS over conventional signal compression techniques are architectural: the CS encoder is made signal independent and computationally inexpensive by shifting the bulk of system complexity to the decoder. While these properties of CS allow signal acquisition and communication in some severely resource-deprived conditions that render conventional sampling and coding impossible, they are accompanied by rather disappointing rate-distortion performance. In the present work we propose a novel coding technique that rectifies, to certain extent, the problem of poor compression performance of CS and at the same time maintains the simplicity and universality of the current CS encoder design. The main innovation is a scheme of progressive fixed-rate scalar quantization with binning that enables the CS decoder to exploit hidden correlations between CS measurements, which was overlooked in the existing literature. Experimental results are presented to demonstrate the efficacy of the new CS coding technique.
A new method for signal sparse decomposition

Increasing attention has been paid to the signal sparse representation based on overcomplete dictionaries in the fields of signal processing. The sparsity degree of decomposed signal is directly related with the dictionary chosen, and the computation speed depends on the sparse decomposition algorithm and the scale of dictionary. Almost all sparse decomposition algorithms available suffer from enormous computational complexity, which severely affects the practicability of these algorithms and limits the development of sparse representation upon an overcomplete dictionary. This paper presents a new method for decomposing a signal upon overcomplete dictionary. This method first constructs a special concatenate dictionary with several orthogonal bases and presents a iterative group matching search algorithm. The experiments results show that our algorithm can reduce the computation burden greatly and is more efficient than MP. This paper also proposes a method to determine a near optimal value of the total number of coefficients.
Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding

Existing approaches toward Image super-resolution (SR) is often either data-driven (e.g., based on internet-scale matching and web image retrieval) or model-based (e.g., formulated as an Maximizing a Posterior estimation problem). The former is conceptually simple yet heuristic, while the latter is constrained by the fundamental limit of frequency aliasing. In this paper, we propose to develop a hybrid approach toward SR by combining those two lines of ideas. More specifically, the parameters underlying sparse distributions of desirable HR image patches are learned from a pair of LR image and retrieved HR images. Our hybrid approach can be interpreted as the first attempt of reconciling the difference between parametric and nonparametric models for low-level vision tasks. Experimental results show that the proposed hybrid SR method performs much better than existing state-of-the-art methods in terms of both subjective and objective image qualities.
Robust adaptive directional lifting wavelet transform for image denoising

Recent researches have shown that the adaptive directional lifting (ADL) can represent edges and textures in images effectively. This makes it possible to separate noise from image signal distinctly in image denoising. However, a key issue named orientation estimation for ADL becomes inefficient and error prone in the noised circumstance. The authors propose a robust adaptive directional lifting-based (RADL) wavelet transform for image denoising by constructing ADL in an anti-noise way. In our method, a simple model of pixel pattern classification is incorporated into orientation estimation module to strengthen the robustness of this algorithm. Moreover, instead of determining the transform strategy based on sub-blocks, RADL is performed on pixel-level to pursue better denoising results. Experimental results show that the proposed technique demonstrates both PSNR and visual quality improvement on images with rich textures.
Altitude Measurement of Low Elevation Target Based on Iterative Subspace Projection

A new temporal-spatial general MUSIC (TS-GMUSIC) method is proposed for the altitude measurement of low-elevation targets. As an expansion of the traditional temporal-spatial process, our proposed method employs the alternate projection (AP) theory to measure the target elevation. Different from conventional AP where the direction vector is iteratively projected to achieve the elevation, we iteratively project the received echoes onto different signal subspace to obtain the elevation. Thus the conventional direction-search of the multiple targets can be simplified into one target parameter estimation. Further we consider the multipath geometry characteristic of low-elevation targets, therefore the direction-search of both direct wave and specular wave is changed into one-dimensional parameter search. The Monte-Carlo simulations prove that the proposed TS-GMUSIC shows better performance than the existing temporal-spatial algorithms. Moreover, the real-data processing of VHF radar demonstrates the validity of the proposed method.
A new multiple description image coding scheme based on Compressive Sensing

This paper proposes a new two-description image coding technique based on Compressive Sensing(CS). In the new multiple description coding scheme, the source image is split into two sub-images with quincunx downsampling operation and further two descriptions are generated by CS measurement. The decoding is accomplished by solving a convex l1 optimization problem. The decoding at each side decoder is done by an interpolation process that exploits inter-description correlation. The new method still makes use of an attractive property of CS that the recovery effect only depends on the received measurement number but not on which measurements are received correctly. Experimental results demonstrate that the proposed image multiple description coding scheme has the advantage of high robustness to packet loss.
Content complexity based just noticeable difference estimation in DCT domain

Just noticeable difference (JND), which reveals the visibility of our human visual system (HVS), is useful for image/video coding. Due to the content complexity, it is hard to accurately estimate the JND thresholds for different image blocks (e.g., edge and texture). Research on cognitive science indicates that the HVS is adaptive to extract the visual regularities for scene perception and understanding. Inspired by this, we analyze the effect of content complexity on visual masking, and calculate the content complexity with the distribution of orientation. Then, by considering the effect from content complexity, contrast sensitivity function, and luminance adaption, a novel JND model in Discrete Cosine Transform (DCT) domain is proposed. Experimental results demonstrate the effectiveness of the proposed model for JND estimation.
The 3-POCs Structure Based GPU Acceleration in Computational Spectral Imaging

In this paper, the computational spectral imaging system is reexamined, and the most time-consuming module, namely the two-step iterative shrinkage/thresholding (TwIST) reconstruction, is accelerated by GPU. The acceleration can be roughly divided into two level: 1) data parallelization and 2)operation parallelization.  by discovering that the observation data array in computational spectral imaging is independent with each other in the norm of the dispersion direction, we propose a strategy to decompose the large scale 2-D observation data array into several small scale overlapped stripes. Considering that the complexity for TwIST is highly non-linear, this divide-conquer strategy can significantly reduce the executing time of TwIST. 2)operation parallelization: we proposed a new 3-projection onto convex sets (POCs) structure for the GPU implementation of TwIST. All the operations are classified into 3 sets which only consist of independent matrix/vector operations. With the above two contributions, the proposed 3-POCs structure based parallel implementation achieves more than 100 times ratio than the CPU based version.
The design of M-channel linear phase wavelet-like filter banks

This paper investigates M-channel linear-phase (LP) wavelet-like filter banks. The term ldquowavelet-likerdquo is justified because the filter bank is of near-perfect-reconstruction (NPR). We firstly derive some conditions for designing M-channel LP NPR filter banks and then propose a design method of wavelet-like filter banks. The proposed wavelet-like filter bank is obtained by imposing regularity constraints on the LP NPR filter bank. By using some simple algebraic transformations, we convert the original filter into the wavelet-like filter without nonlinear optimization. With this method, we not only preserve the desired LP property, but also obtain good NPR property as the original NPR filter bank, such as high stopband attenuation. Finally, examples show that LP wavelet-like filter banks with even and odd numbers of channels are capable of achieving different degrees of regularity.
Multiple-Stations Scalable Transmission With Compressed Sensing for Near-Space Communication

Near-space communication suffers from communication blackout, which reduces the communication quality. To solve this problem, an efficient Multiple-Stations Scalable Transmission scheme based on compressed sensing theory is proposed. The proposed scheme exhibits two advantages. First, multiple-station receivers are disposed at different locations to accomplish continuous and real-time communication with the hypersonic vehicle. Second, by utilizing the democracy and scalability of the CS, a scalable CS-based coding method is developed in which a two-layer structure is adopted to improve the coding efficiency. Finally, simulation results show that the proposed scheme can achieve a reliable communication in the presence of data loss caused by communication interruption.
Perceptual CU Size Decision and Fast Prediction Mode Decision Algorithm for HEVC Intra Coding

Intra coding has been significantly improved in HEVC over H.264/AVC with quad-tree based coding unit (CU) structure from size 64×64 to 8×8 and more prediction modes. However, these techniques cause a dramatic increase in computational complexity. In this paper, a novel intra coding algorithm is proposed consists of perceptual CU size decision algorithm and fast intra prediction mode decision algorithm. Firstly, based on the visual saliency detection, an adaptive and perceptual CU size decision method is proposed to alleviate intra encoding complexity. Furthermore, a fast intra prediction mode decision algorithm with step halving rough mode decision method is presented to selectively check the potential modes and effectively reduce the complexity of computation. Experimental results show that our proposed method reduces the computational complexity of the current HM to about 54.18% in encoding time with only 0.36% increases in BD rate and reasonable peak signal-to-noise ratio losses.
Nonlocally Centralized Sparse Representation for Image Restoration

Sparse representation models code an image patch as a linear combination of a few atoms chosen out from an over-complete dictionary, and they have shown promising results in various image restoration applications. However, due to the degradation of the observed image (e.g., noisy, blurred, and/or down-sampled), the sparse representations by conventional models may not be accurate enough for a faithful reconstruction of the original image. To improve the performance of sparse representation-based image restoration, in this paper the concept of sparse coding noise is introduced, and the goal of image restoration turns to how to suppress the sparse coding noise. To this end, we exploit the image nonlocal self-similarity to obtain good estimates of the sparse coding coefficients of the original image, and then centralize the sparse coding coefficients of the observed image to those estimates. The so-called nonlocally centralized sparse representation (NCSR) model is as simple as the standard sparse representation model, while our extensive experiments on various types of image restoration problems, including denoising, deblurring and super-resolution, validate the generality and state-of-the-art performance of the proposed NCSR algorithm.
Mixed Noise Removal via Laplacian Scale Mixture Modeling and Nonlocal Low-Rank Approximation

Recovering the image corrupted by additive white Gaussian noise (AWGN) and impulse noise is a challenging problem due to its difficulties in an accurate modeling of the distributions of the mixture noise. Many efforts have been made to first detect the locations of the impulse noise and then recover the clean image with image in painting techniques from an incomplete image corrupted by AWGN. However, it is quite challenging to accurately detect the locations of the impulse noise when the mixture noise is strong. In this paper, we propose an effective mixture noise removal method based on Laplacian scale mixture (LSM) modeling and nonlocal low-rank regularization. The impulse noise is modeled with LSM distributions, and both the hidden scale parameters and the impulse noise are jointly estimated to adaptively characterize the real noise. To exploit the nonlocal self-similarity and low-rank nature of natural image, a nonlocal low-rank regularization is adopted to regularize the denoising process. Experimental results on synthetic noisy images show that the proposed method outperforms existing mixture noise removal methods.
Enhanced just noticeable difference model with visual regularity consideration

Just noticeable difference (JND) reveals the visibility of our human visual system (HVS), below which changes cannot be perceived by the human. Though dozens of JND estimation models have been introduced during the past decade, how to accurately estimate the JND thresholds for different content regions (e.g., edge and texture region) is still an open problem. Research on cognitive science indicates that the HVS is adaptive to extract the visual regularities from an input scene for content perception and understanding. Thus, we analyze the effect of content regularity on visual sensitivity, and suggest that the visual regularity is another important factor that determines the JND threshold. According to the orientation distributions of local regions, the content regularities are firstly calculated. Then, by considering the effect from content regularity, luminance adaptation, and contrast masking, a novel JND model is proposed. Experimental results demonstrate that the proposed model can effectively estimate the JND thresholds of regions with different visual contents.
High-Speed Hyperspectral Video Acquisition By Combining Nyquist and Compressive Sampling

We propose a novel hybrid imaging system to acquire 4D high-speed hyperspectral (HSHS) videos with high spatial and spectral resolution. The proposed system consists of two branches: one branch performs Nyquist sampling in the temporal dimension while integrating the whole spectrum, resulting in a high-frame-rate panchromatic video; the other branch performs compressive sampling in the spectral dimension with longer exposures, resulting in a low-frame-rate hyperspectral video. Owing to the high light throughput and complementary sampling, these two branches jointly provide reliable measurements for recovering the underlying HSHS video. Moreover, the panchromatic video can be used to learn an over-complete 3D dictionary to represent each band-wise video sparsely, thanks to the inherent structural similarity in the spectral dimension. Based on the joint measurements and the self-adaptive dictionary, we further propose a simultaneous spectral sparse (3S) model to reinforce the structural similarity across different bands and develop an efficient computational reconstruction algorithm to recover the HSHS video. Both simulation and hardware experiments validate the effectiveness of the proposed approach. To the best of our knowledge, this is the first time that hyperspectral videos can be acquired at a frame rate up to 100fps with commodity optical elements and under ordinary indoor illumination.
Nonlocal center-surround reconstruction-based bottom-up saliency estimation

The center-surround comparison principle is widely used in existing bottom-up saliency estimation models. However, most of them are based on local image processing techniques which are hard to handle texture regions well as a relatively large neighborhood is required to represent textures. In this paper, we propose a nonlocal patch-based reconstruction approach to reformulate the center-surround comparison. In the proposed approach, the saliency is measured by the reconstruction residual of representing the central patch with a linear combination of its surrounding patches. As a generalization of Itti et al.'s classical center-surround comparison scheme, the proposed approach performs well on images with symmetric structures where Itti et al.'s method fails, as well as on general natural images. Numerical experiments show the proposed approach produces better results compared to the state-of-the-art algorithms on several public databases.
A pipelined architecture for 4×4 intra frame mode decision in the high efficiency video coding

Mode decision in High Efficient Video Coding (HEVC) is occupied more than half of the computational complexity in intra frame coding. Block size of 4×4 is the most frequently used block in HM. In this paper, we proposed a pipelined architecture for the 4×4 intra frame mode decision in HEVC to improve the computational capability. This novel architecture consists of six-stage pipelines, and each of the pipelines can be accomplished within 24 clock cycles. In the pipeline of prediction procedure, we proposed a folded project-skip architecture for prediction. It can save the processing latency and the registers considerably. We also proposed a simplified CAVLC with low complexity in the pipeline of bits estimation procedure. The architecture for mode decision has been evaluated with TSMC 0.13μm CMOS technology. Synthesized results show that the proposed architecture only needs 99K logic gates for modes decision and can run at 165 MHz operation frequency.