nils-herrmann/clean_scirepeval huggingface dataset:
Compress compound images in H.264/MPEG-4 AVC by fully exploiting spatial correlation

Compound images consist of text, graphics and natural images, which present strong anisotropic features. It makes existing image coding standards inefficient on compressing them. To solve the problem, this paper proposes a novel coding scheme based on the H.264 intra-frame coding. Two new intra modes are proposed to better exploit spatial correlations in compound images. The first is residual scalar quantization (RSQ) mode, where intra-predicted residues are directly quantized and entropy coded. The second is base colors and index map (BCIM) mode that can be viewed as an adaptive vector quantization. In this mode, an image block is represented by several representative colors, called as base colors, and an index map to compress. Two new modes as well as previous intra modes in H.264 are selected by the rate-distortion optimization (RDO) method in each block. Experimental results show that the proposed scheme not only improves the coding efficiency even more than 10dB for compound images but also keeps the similar performance as H.264 for natural images.
Adaptive Nonlocal Sparse Representation for Dual-Camera Compressive Hyperspectral Imaging

Leveraging the compressive sensing (CS) theory, coded aperture snapshot spectral imaging (CASSI) provides an efficient solution to recover 3D hyperspectral data from a 2D measurement. The dual-camera design of CASSI, by adding an uncoded panchromatic measurement, enhances the reconstruction fidelity while maintaining the snapshot advantage. In this paper, we propose an adaptive nonlocal sparse representation (ANSR) model to boost the performance of dual-camera compressive hyperspectral imaging (DCCHI). Specifically, the CS reconstruction problem is formulated as a 3D cube based sparse representation to make full use of the nonlocal similarity in both the spatial and spectral domains. Our key observation is that, the panchromatic image, besides playing the role of direct measurement, can be further exploited to help the nonlocal similarity estimation. Therefore, we design a joint similarity metric by adaptively combining the internal similarity within the reconstructed hyperspectral image and the external similarity within the panchromatic image. In this way, the fidelity of CS reconstruction is greatly enhanced. Both simulation and hardware experimental results show significant improvement of the proposed method over the state-of-the-art.
Narrowband ultrasonic detection with high range resolution: separating echoes via compressed sensing and singular value decomposition

It is well known that broader bandwidth leads to higher range resolution, but in ultrasonic flaw detection, the bandwidth is often limited. That is, the upper frequency of the transmitted signal is bounded by the attenuation of highfrequency ultrasound, especially when the penetration depth is large. In this case, only the band-limited signals are suitable for far-field detection. Fortunately, theoretical analysis in this paper shows that narrowband detection has greater potential for obtaining high range resolution compared with broadband detection. However, it is difficult to fully utilize this potential because of the severe overlap of narrowband echoes. As a result, we propose a novel approach for separating highly overlapping narrowband echoes in this paper to improve the resolution of range imaging. In our approach, in addition to a formulation following the emerging theory of sparse representation and compressed sensing, singular value decomposition is also employed to capture the main features of the signals, making this approach robust to moderate signal distortions. Numerous simulations and experiments on real data show that the proposed approach is able to fully utilize the potential of narrowband detection and outperforms other competitive methods in terms of stability and accuracy.
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 paper, 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 nonzero 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. An efficient algorithm is presented to solve the compressive image recovery (CIR) problem using the refined models. Experimental results on both simulated compressive sensing (CS) image data and real CS image data show that the new CIR method significantly outperforms existing CIR methods in both PSNR and visual quality.
SAR Image Despeckling Based on the Nonlocally Centralized Sparse Representation Model

In this paper, a novel SAR image despeckling method based on the nonlocal centralized sparse representation (NCSR) is presented. NCSR model is initially proposed for image restoration, exhibiting excellent denoising performance for nature images corrupted by additive Gaussian noise. However, SAR images, whose speckle noise is multiplicative and follows non-Gaussian distribution, are very different from the nature images. Considering the properties of speckle, SAR images are pre-processed, and then based on the generalized likelihood ratio (GLR) criteria, a new metric function is developed for acquiring a better estimates of the sparse coding coefficients of each corresponding noiseless SAR image patch. Experimental results show that the proposed method has a good capability of speckle smoothing in homogeneous region, as well as edge and texture preservation.
Sparsity-based image deblurring with locally adaptive and nonlocally robust regularization

Important structures in photographic images such as edges and textures are jointly characterized by local variation and nonlocal invariance (similarity). Both of them provide valuable heuristics to the regularization of image restoration process. In this paper, we propose to explore two sets of complementary ideas: 1) locally learn PCA-based dictionaries and estimate the sparsity regularization parameters for each coefficient; and 2) nonlocally enforce the invariance constraint by introducing a patch-similarity based term into the cost functional. The minimization of this new cost functional leads to an iterative thresholding-based image deblurring algorithm and its efficient implementation is discussed. Our experimental results have shown that the proposed scheme significantly outperforms several leading deblurring techniques in the literature on both objective and visual quality assessments.
Edge-Based Perceptual Image Coding

We develop a novel psychovisually motivated edge-based low-bit-rate image codec. It offers a compact description of scale-invariant second-order statistics of natural images, the preservation of which is crucial to the perceptual quality of coded images. Although being edge based, the codec does not explicitly code the edge geometry. To save bits on edge descriptions, a background layer of the image is first coded and transmitted, from which the decoder estimates the trajectories of significant edges. The edge regions are then refined by a residual coding technique based on edge dilation and sequential scanning in the edge direction. Experimental results show that the new image coding technique outperforms the existing ones in both objective and perceptual quality, particularly at low bit rates.
LDA based color information fusion for visual objects tracking

In this paper, an approach for object tracking is proposed based on the online color information fusion scheme. The fusion scheme, is performed by projecting multi-channel color images to a one-dimensional pseudo gray scale space. This dimensionality reduction simplifies the designation of the tracking algorithm. The fusing coefficients are determined by taking the Fisher linear discriminant analysis to maximize the discriminative capability after taking the projection. The robustness of the approach lies in exploiting the appearance discrimination of the object in cluttered scenarios with varying illumination conditions. This scheme is embedded into a mean-shift tracking system and the experimental results show our scheme can enhance the discriminant characters in the changing environment and hence producing robust tracking results.
Temporal event analysis and program understanding

A temporal event analysis approach to program understanding is described. Program understanding is viewed as a sequence of episodes in which the programmer concludes that an informal event occurs that corresponds to some part of the code. This can be viewed as accepting that the code is an adequate definition of the meaning of the informal event. Often, such a definition is contingent upon working hypotheses that describe other informal program properties that should be verified in order to confirm the validity of the understanding process. Verification of working hypotheses may depend on the formulation of additional definitions or working hypotheses. The understanding process can be assisted through the use of a documentation language for describing events and hypotheses, and a hypothesis verification tool. The paper describes a temporal event language in which hypotheses are formulated in terms of expected event sequences. A hypothesis verification tool was built, and experimentation was carried out on a set of programs. The tool was found to be very useful in understanding the detailed, control oriented aspects of a program. Program faults were discovered in every program that was analyzed, indicating that it facilitates a deep level of understanding.
An iterative representation learning framework to predict the sequence of eye fixations

Visual attention is a dynamic search process of acquiring information. However, most previous studies have focused on the prediction of static attended locations. Without considering the temporal relationship of fixations, these models usually cannot explain the dynamic saccadic behavior well. In this paper, an iterative representation learning framework is proposed to predict the saccadic scanpath. Within the proposed framework, saccade can be explained as an iterative process of finding the most uncertain area and updating the representation of scenes. In implementation, a deep autoencoder is employed for representation learning. The current fixation is predicted to be the most salient pixel, with saliency estimated by the reconstruction residual of the deep network. Image patches around this fixation are then sampled to update the network for the selection of subsequent fixations. Compared with existing models, the proposed model shows the state-of-the-art performance on several public data sets.
Nonlocal back-projection for adaptive image enlargement

This paper presents a novel non-local iterative back-projection (NLIBP) algorithm for image enlargement. The iterative back-projection (IBP) technique iteratively reconstructs a high resolution (HR) image from its blurred and downsampled low resolution (LR) counterpart. However, the conventional IBP methods often produce many “jaggy” and “ringing” artifacts because the reconstruction errors are back projected into the reconstructed image isotropically and locally. In natural images, usually there exist many non-local redundancies which can be exploited to improve the image reconstruction quality. Therefore, we propose to incorporate adaptively the non-local information into the IBP process so that the reconstruction errors can be reduced. Experimental results demonstrated that the proposed NLBP can reconstruct faithfully the HR images with sharp edges and texture structures. It outperforms the state-of-the-art methods in both PSNR and visual perception.
Image Super-Resolution With Parametric Sparse Model Learning

Recovering a high-resolution (HR) image from its low-resolution (LR) version is an ill-posed inverse problem. Learning accurate prior of HR images is of great importance to solve this inverse problem. Existing super-resolution (SR) methods either learn a non-parametric image prior from training data (a large set of LR/HR patch pairs) or estimate a parametric prior from the LR image analytically. Both methods have their limitations: the former lacks flexibility when dealing with different SR settings; while the latter often fails to adapt to spatially varying image structures. In this paper, we propose to take a hybrid approach toward image SR by combining those two lines of ideas-that is, a parametric sparse prior of HR images is learned from the training set as well as the input LR image. By exploiting the strengths of both worlds, we can more accurately recover the sparse codes and therefore HR image patches than conventional sparse coding approaches. Experimental results show that the proposed hybrid SR method significantly outperforms existing model-based SR methods and is highly competitive to current state-of-the-art learning-based SR methods in terms of both subjective and objective image qualities.
Unequal error protection for scalable video storage in the cloud

Redundancy is necessary for a storage system to recover from errors. The frequent errors in large-scale systems, e.g. cloud, make it desired to reduce the recovery cost. Among all kinds of data stored in the cloud, video takes a large portion due to its large data volume. The other characteristic of video is that a certain distortion can be tolerated. This paper investigates using scalable video representation and unequal error protection scheme to reduce the storage and recovery costs in the cloud. By introducing more protection for the base layer and less on the enhancement layers, it can achieve a better tradeoff between storage and reconstruction costs although the reliability for the enhancement layer sacrifices a little. Simulation results based on local reconstruction codes (LRC) show that comparing with the existing (12, 2, 2) LRC code in Windows Azure Storage, the reconstruction cost can be reduced from 6x to 3x at the same storage cost at the expense of possible video quality loss.
Unequal Error Protection for Scalable Video Storage in the Cloud

Redundancy is necessary for a storage system to achieve reliability. Frequent errors in large-scale storage systems, for example, cloud, make it desirable to reduce the cost of recovery. Among all types of data in cloud storage, videos generally occupy significant amounts of space due to high volumes and the rapid development of video sharing and video-on-demand services. Unlike general data, videos can tolerate a certain level of quality degradation. This paper investigates multilayer video representations, such as scalable videos and simulcast streaming, and proposes an unequal error protection scheme based on local reconstruction codes (LRC) for video storage. By providing less protection for less important layers or video copies, a better tradeoff between storage and repair cost is achieved. Both theoretical and simulation results show that such a tradeoff can be achieved over the LRC with equal error protection, though the recovered video quality might be slightly lower in rare cases.
Recent Advances and New Design Method in Nonuniform Filter Banks

This paper mainly presents some recent advances in designing nonuniform filter banks (NUFBs), including direct and indirect methods, along with the detailed review on the typical design methods. Starting with the description of the motivations behind using NUFBs in real applications, we identify some problems existing in the current work. As a new contribution, we then propose a simple design method of near perfect reconstruction (NPR) NUFBs with a linear phase (LP) property called partial cosine modulation. Here we design the first and last analysis/synthesis filters separately, while obtaining the others via cosine modulation of several prototype filters. A design example is given to demonstrate the effectiveness and simplicity of this approach
Compress Compound Images in H.264/MPGE-4 AVC by Exploiting Spatial Correlation

Compound images are a combination of text, graphics and natural image. They present strong anisotropic features, especially on the text and graphics parts. These anisotropic features often render conventional compression inefficient. Thus, this paper proposes a novel coding scheme from the H.264 intraframe coding. In the scheme, two new intramodes are developed to better exploit spatial correlation in compound images. The first is the residual scalar quantization (RSQ) mode, where intrapredicted residues are directly quantized and coded without transform. The second is the base colors and index map (BCIM) mode that can be viewed as an adaptive color quantization. In this mode, an image block is represented by several representative colors, referred to as base colors, and an index map to compress. Every block selects its coding mode from two new modes and the previous intramodes in H.264 by rate-distortion optimization (RDO). Experimental results show that the proposed scheme improves the coding efficiency even more than 10 dB at most bit rates for compound images and keeps a comparable efficient performance to H.264 for natural images.
Visual Orientation Selectivity Based Structure Description

The human visual system is highly adaptive to extract structure information for scene perception, and structure character is widely used in perception-oriented image processing works. However, the existing structure descriptors mainly describe the luminance contrast of a local region, but cannot effectively represent the spatial correlation of structure. In this paper, we introduce a novel structure descriptor according to the orientation selectivity mechanism in the primary visual cortex. Research on cognitive neuroscience indicate that the arrangement of excitatory and inhibitory cortex cells arise orientation selectivity in a local receptive field, within which the primary visual cortex performs visual information extraction for scene understanding. Inspired by the orientation selectivity mechanism, we compute the correlations among pixels in a local region based on the similarities of their preferred orientation. By imitating the arrangement of the excitatory/inhibitory cells, the correlations between a central pixel and its local neighbors are binarized, and the spatial correlation is represented with a set of binary values, which is named the orientation selectivity-based pattern. Then, taking both the gradient magnitude and the orientation selectivity-based pattern into account, a rotation invariant structure descriptor is introduced. The proposed structure descriptor is applied in texture classification and reduced reference image quality assessment, as two different application domains to verify its generality and robustness. Experimental results demonstrate that the orientation selectivity-based structure descriptor is robust to disturbance, and can effectively represent the structure degradation caused by different types of distortion.
Compressive sensing via reweighted TV and nonlocal sparsity regularisation

Total variation (TV) regularisation has been widely used for compressive sensing (CS) reconstruction. However, since TV regularisers favour piecewise constant solutions, they tend to produce over-smoothed image edges. To overcome this drawback, proposed is a novel iteratively reweighted TV regulariser for CS reconstruction. Spatially adaptive weights are computed towards a maximum a posteriori estimation of the image gradients. To exploit the nonlocal redundancy, effective nonlocal sparsity regularisation has also been introduced into the proposed objective function. Experimental results demonstrate that the proposed CS reconstruction method outperforms significantly existing TV-based CS reconstruction methods.
Fast ISAR Imaging Based on Enhanced Sparse Representation Model

Traditional sparse representation-(SR) based inverse synthetic aperture radar (ISAR) imaging schemes can achieve significant performance, but they suffer from high costs of memory and computational complexity, because the SR of a 2-D image is converted into that of a 1-D vector. Instead of memory consuming vector operations, we propose a fast ISAR imaging algorithm, where the decomposition and reconstruction of a 2-D scene is implemented using matrix operations directly. Besides the spatial sparsity of a scene, its structural sparsity is presented using the range profile of the scene, where both can be used to enhance sparsity exploitation during image reconstruction. Also, benefitting from the structural sparsity of the range profile, the target energy can be accumulated during the process, which further improves performance. Compared to available SR-based ISAR imaging algorithms, the proposed algorithm reduces both memory costs and computational complexity significantly, which is proven using simulated and real data.
An efficient VLSI architecture for 4×4 intra prediction in the High Efficiency Video Coding (HEVC) standard

Intra prediction with fine directions is a critical feature in the new High Efficiency Video Coding (HEVC) standard because it provides significant performance gain. Different from the intra prediction in the H.264/AVC, this approach is more complicated in terms of computation and memory access, which makes the VLSI design very difficult. In this paper, we propose an efficient uniform architecture for all of the 4×4 intra directional modes. The architecture is implemented by a register array and a flexible reference sample selection technique. This novel architecture does not need to project the samples from the side reference to the main reference. Thus, it reduces the processing latency and the number of registers considerably. The proposed architecture has been implemented with TSMC 0.13μm CMOS technology. Simulation results show that the proposed architecture only needs 9020 logic gates for 17 directional modes and can run at 150 MHz operation frequency.
Low complexity DOA estimation approach through multitask Bayesian compressive sensing strategies

Based on the Multitask Bayesian Compressive Sensing (MT-BCS) framework, a novel DOA estimation approach for planar array is proposed in this paper. Different from the traditional CS-based DOA model, where the spatial observation is characterized in one large scale matrix, to reduce the complexity, a separable observation structure is proposed, which separates the joint spatial observation into two individual parts, and thus, the large scale matrix can be split into two small scale matrices. In addition, the Multitask Bayesian Compressive Sensing framework is engaged to build a MT-BCS-based DOA estimation scheme (MT-BCS-DOA). The simulation results show the superior capability of the proposed approach.
Research & Realization on Key Skills Development: Based on Digital Signal Processing

The skills are key to the future success of graduates whatever they intend to do in later life. How to develop the key skills are discussed in this paper. We have reformed teaching methods through the subject called digital signal processing by bilingual teaching. More ways of assessments have been adopted to inspire the students' interest in learning. With the proceeding of the subject, it is the process for the students learn how to learn and how to cooperate with others.