nils-herrmann/clean_scirepeval huggingface dataset:
Reduced-reference image quality assessment based on entropy differences in DCT domain

Reduced-reference image quality assessment (RR-IQA) algorithm aims to automatically evaluate the image quality using only partial information about the reference image. In this paper, we propose a new RR-IQA metric by employing the entropy features of each frequency band in the DCT domain. It is well known that human eyes have different sensitivity to different bands, and distortions on each band result in individual quality degradations. Therefore, we suggest to separately compute the visual information degradations on different band for quality assessment. The degradations on each DCT band are firstly analyzed according to the entropy difference. And then, the quality score is obtained using the weighted sum of the entropy difference of each band from low frequency to high frequency. Experimental results on several public image databases show that the proposed method uses limited reference data (8 values) and performs highly consistent with human perception.
Improvement of factorization for two-channel perfect reconstruction FIR filter banks

There is an increasing interest in designing structurally perfect reconstruction (PR) filter banks because the system can be implemented by using sum of powers-of-two (SOPOT) coefficients. The structurally PR filter banks can be designed by factorization based on the lifting scheme. But there exist some problems that are addressed in this paper. Improvement of the factorization to solve the problems is proposed. The procedures of proof for the improvement are given. Finally, the given examples show that the proposed method is effective.
Direct Design of Near Perfect Reconstruction Linear Phase Nonuniform Filter Banks with Rational Sampling Factors

A direct method of designing linear phase (LP) near perfect reconstruction (NPR) nonuniform filter banks (NUFBs) with rational sampling factors is presented. Conditions on the possible sampling factors of NUFBs are given. The band position relations and phase relations of the significant aliasing components are also analyzed. Based on these two relations, we derive a necessary condition on the elimination of significant aliasing distortion (ALD). Meanwhile LP property is met. Moreover, a set of relations of the filters are presented to minimize the ALD further. In our proposed method, the LP NUFBs with high stopband attenuation and low system delay can be easily designed without nonlinear optimization procedure
Robust compressive multi-input–multi-output imaging

Canonical multi-input–multi-output (MIMO) imaging methods suffer from limited resolution, poor robustness against noise and high computational complexity, especially when the array aperture is limited (affecting angular resolution) and the number of snapshots is limited (affecting Doppler resolution). In this study, the authors discuss a new range-angle-Doppler monostatic MIMO imaging method through adaptive estimation of the generalised Cauchy prior distribution (GCD). The superiority of GCD-based model over existing l p -norm-based model (which actually assumes the prior as general Gaussian distribution) is theoretically verified through the issue of signal compressibility. The authors adapt a reweighted l2-norm iterative algorithm to solve the model. In our model the authors do not pre-define the scaling factor of the prior distribution and, during the iteration, the authors use a novel ‘quantile-of-OS’ method to adaptively estimate the scaling parameter of the prior distribution, enhancing the robustness of the method. Simulation results verify the image quality and speed advantages of the proposed method.
Spatial-Temporal Color Video Reconstruction From Noisy CFA Sequence

Single-sensor digital video cameras use a color filter array (CFA) to capture video and a color demosaicking (CDM) procedure to reproduce the full color sequence. The reproduced video frames suffer from the inevitable sensor noise introduced in the video acquisition process. This paper presents a spatial-temporal denoising and demosaicking scheme that works without explicit motion estimation. We first perform patch based denoising on the mosaic CFA video. For each CFA patch to be denoised, similar patches are selected within a local spatial-temporal neighborhood. The principal component analysis is performed on the selected patches to remove noise. We then apply an initial single-frame CDM to the denoised CFA data, and subsequently post-process the demosaicked frames by exploiting the spatial-temporal redundancy to reduce the color artifacts. The experimental results on simulated and real noisy CFA sequences demonstrate that the proposed spatial-temporal CFA video denoising and demosaicking scheme can significantly reduce the noise-caused color artifacts and effectively preserve the image edge structures.
Lossy-to-Lossless Hyperspectral Image Compression Based on Multiplierless Reversible Integer TDLT/KLT

We proposed a new transform scheme of multiplierless reversible time-domain lapped transform and Karhunen-Loeve transform (RTDLT/KLT) for lossy-to-lossless hyperspectral image compression. Instead of applying discrete wavelet transform (DWT) in the spatial domain, RTDLT is applied for decorrelation. RTDLT can be achieved by existing discrete cosine transform and pre- and postfilters, while the reversible transform is guaranteed by a matrix factorization method. In the spectral direction, reversible integer low-complexity KLT is used for decorrelation. Owing to completely reversible transform, the proposed method can realize progressive lossy-to-lossless compression from a single embedded code-stream file. Numerical experiments on benchmark images show that the proposed transform scheme performs better than 5/3DWT-based methods in both lossy and lossless compressions, comparable with the optimal 9/7DWT-FloatKLT-based lossy compression method.
Sparsity-based image denoising via dictionary learning and structural clustering

Where does the sparsity in image signals come from? Local and nonlocal image models have supplied complementary views toward the regularity in natural images — the former attempts to construct or learn a dictionary of basis functions that promotes the sparsity; while the latter connects the sparsity with the self-similarity of the image source by clustering. In this paper, we present a variational framework for unifying the above two views and propose a new denoising algorithm built upon clustering-based sparse representation (CSR). Inspired by the success of l1-optimization, we have formulated a double-header l1-optimization problem where the regularization involves both dictionary learning and structural structuring. A surrogate-function based iterative shrinkage solution has been developed to solve the double-header l1-optimization problem and a probabilistic interpretation of CSR model is also included. Our experimental results have shown convincing improvements over state-of-the-art denoising technique BM3D on the class of regular texture images. The PSNR performance of CSR denoising is at least comparable and often superior to other competing schemes including BM3D on a collection of 12 generic natural images.
Dynamic Range Reduction of SAR Image via Global Optimum Entropy Maximization With Reflectivity-Distortion Constraint

The visualization of synthetic aperture radar (SAR) images plays a critical role in remote sensing applications. To effectively obtain the image suitable for human observation, this paper introduces a new SAR image visualization algorithm to map the high dynamic range SAR amplitude values to low dynamic range displays via reflectivity distortion preserved entropy maximization. Its designed objective is to present the maximal amount of information content in the displayed image, and being optimal in an information theoretical sense, as well as restricting the upper bound of the reflection distortion caused by tone mapping. The resulting optimization problem can be graph theoretically modeled as a  $K$ -edges maximum weight path problem in a directed acyclic graph, and it can be solved efficiently by dynamic programming in real time. Empirical evidences are provided to demonstrate the superior visual quality obtained by our new visualization technique.
Binned Progressive Quantization for Compressive Sensing

Compressive sensing (CS) has been 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 this paper, we propose a novel coding technique that rectifies, to a 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. Encouragingly, on some test images, the new CS technique matches or even slightly outperforms JPEG.
Saliency change based reduced reference image quality assessment

The image quality assessment (IQA) technique, which aims to perform coherently with subjective perception, is useful in quality-orientated image processing systems. In this paper, we suggest to take the saliency change into account for reduced reference (RR) IQA model. Generally, a saliency region will attract more attention, and our human vision is more sensitive to quality degradation on such region. Inspired by this, saliency values are firstly used to highlight these sensitive regions, and a local saliency weighted histogram (LSWH) based on visual orientation pattern is generated for visual feature extraction. Next, strong distortion may change the saliency from the reference to the distorted images. Thus, the saliency of each visual orientation pattern is measured, and a global saliency based histogram (GSBH) is created. Finally, by combining the LSWH and GSBH, a novel IQA model for reduced reference is introduced. Experimental results on five publicly available databases demonstrate that the proposed model uses only several values (9 values) as reference information, and performs consistently with subjective perception.
Design of Directional Filter Banks With Arbitrary Number of Subbands

This correspondence proposes a novel method for designing directional filter banks (DFBs) with arbitrary number of subbands. Its key feature is the ability to decompose images into arbitrary directionally oriented subbands. The proposed approach is based on the pseudo-polar Fourier transform (PPFT) and one-dimensional (1-D) filter banks (FBs). We take some modifications on the PPFT and then employ 1-D FBs to the modified PPFT. With these operations, the two-dimensional (2-D) DFBs are obtained and the design of them is converted to that of 1-D FBs plus a modified PPFT. Since the number of channels of 1-D FBs can be arbitrary, the 2-D DFBs with arbitrary number of subbands can be achieved which is highly expected for directional representations of images. Two examples on directional feature extractions illustrate that the proposed non-2n channel DFBs can capture directional information more flexibly than the existing methods.
Phase Retrieval From Multiple-Window Short-Time Fourier Measurements

In this paper, we introduce two undirected graphs depending on supports of signals and windows, and we show that the connectivity of those graphs provides either necessary or sufficient conditions to phase retrieval of a signal from magnitude measurements of its multiple-window short-time Fourier transform. Also, we propose an algebraic reconstruction algorithm, and provide an error estimate to our algorithm when magnitude measurements are corrupted by deterministic/random noises.
A Distributed ADMM Approach With Decomposition-Coordination for Mobile Data Offloading

Mobile data offloading allows alternative network systems such as Wi-Fi hotspot access points (APs) to offload the traffic that is originally targeted for the cellular network operators (CNOs). It can alleviate the cellular network congestions and enhance users’ quality-of-service. One of the main challenges of the mobile data offloading is to develop simple and distributed traffic allocation strategies that can optimally allocate the cellular data traffic from multiple CNOs to the APs. In this work, we propose a distributed optimization framework with decomposition-coordination to solve this problem. In our framework, the utility maximization problem of the cellular network system with mobile data offloading is formulated as a nonsmooth convex optimization problem. We then divide this problem into a set of subproblems, and each of them is solved by a CNO or an AP using its local information. All the subproblems are coordinated with each other by a virtual data offloading coordinator (VDOC), which can collect the intermediate calculation values from CNOs and APs. The VDOC will then feedback the coordination results calculated from the collected values to the corresponding CNOs and APs. We propose two distributed algorithms that can achieve the global optimization solution under different scenarios. The first one is the multiblock proximal Jacobi alternating direction method of multipliers (ProxJ-ADMM). In this algorithm, the communication between the VDOC and CNOs or APs is assumed to be fully synchronized, and the VDOC will only feedback the coordination result after successfully receiving all the values from APs and CNOs. We relax this assumption by introducing the second algorithm referred to as the distributed asynchronized ADMM (Async-ADMM) algorithm. In this algorithm, the coordination between the VDOC and APs or CNOs does not need to be perfectly synchronized and the VDOC will feedback a coordination result whenever it receives a value from at least one AP or CNO. We prove that both algorithm can achieve the global optimal solution. We present the numerical results to verify the performance of our proposed approaches under various network settings and conditions.
Nonuniform Directional Filter Banks With Arbitrary Frequency Partitioning

Directional filter banks (DFBs) are highly desired in directional representation of images. In this correspondence, we propose a 2-D nonsubsampled nonuniform directional filter bank (NUDFB) and its design method. The proposed NUDFB has nonuniform wedge-shaped subbands and allows arbitrary frequency partitioning schemes. It can extract directional information according to the directional distribution of images. This attractive advantage cannot be achieved by the existing directional transforms. The design method of the proposed NUDFB is based upon the pseudopolar Fourier transform. By utilizing the geometry property of the pseudopolar grid, we employ a 1-D nonsubsampled nonuniform filter bank to obtain a set of nonuniform wedge-shaped subbands. During the design process, only 1-D operations are involved and, thus, the difficulty encountered in the design of 2-D fan filters is avoided. To demonstrate the potential of the proposed NUDFB, an example on image directional decomposition is given.
Color-Guided Depth Recovery via Joint Local Structural and Nonlocal Low-Rank Regularization

High-quality depth recovery from RGB-D data has received increasingly more attention in recent years due to their wide applications from depth-based image rendering to three-dimensional imaging and video. Sharp contrast between high-quality color images and low-quality depth maps presents severe challenges to the development of color-guided depth recovery techniques. Previous works have emphasized either locally varying characteristics of color-depth dependence or nonlocal similarities around the discontinuities of the scene geometry. Therefore, it is desirable to exploit both local and nonlocal structural constraints for optimizing the performance of color-guided depth recovery. In this work, we propose a unified variational approach via joint local and nonlocal regularization. The local regularization term consists of two complementary parts—one characterizing the color-depth dependence in the gradient domain and the other in the spatial domain; nonlocal regularization involves a low-rank constraint suitable for large-scale depth discontinuities. Extensive experimental results are reported to show that our approach outperforms several existing state-of-the-art depth recovery methods on both synthetic and real-world data sets.
High-Resolution Imaging Via Moving Random Exposure and Its Simulation

In this correspondence, we introduce a new imaging method to obtain high-resolution (HR) images. The image acquisition is performed in two stages, compressive measurement and optimization reconstruction. In order to reconstruct HR images by a small number of sensors, compressive measurements are made. Specifically, compressive measurements are made by a low-resolution (LR) camera with randomly fluttering shutter, which can be viewed as a moving random exposure pattern. In the optimization reconstruction stage, the HR image is computed by different models according to the prior knowledge of scenes. The proposed imaging method offers a new way of acquiring HR images of essentially static scenes when the camera resolution is limited by severe constraints such as cost, battery capacity, memory space, transmission bandwidth, etc. and when the prior knowledge of scenes is available. The simulation results demonstrate the effectiveness of the proposed imaging method.
Learning Parametric Sparse Models for Heavy Noisy Removal From Images

Despite rapid advances in the field of image denoising, heavy noise removal has remained an under-explored area. When the strength of noise becomes comparable to or even more dominating than that of signal, restoration of important structures from such heavily-contaminated images becomes more challenging. Existing model-based and learning-based image denoising techniques often cannot delivery satisfactory results, which call for new insights and algorithmic tools. In this paper, we propose to remove heavy noise with the help of similar images retrieved from datasets, which leads to a hybrid approach combining sparsity-based and learning-based methods. Specifically, the parametric sparse prior of underlying clean image is learned from the retrieved reference images and the input noisy image. Leveraging prior from the reference images, the estimated parametric model contains much more accurate information of image details. When compared against existing conventional methods, the proposed hybrid approach is more capable of restoring fine-detailed structures in the presence of heavy noise. Experimental results show that the proposed method dramatically outperforms current state-of-the-art image denoising methods both subjectively and objectively.
Adaptive Nonseparable Interpolation for Image Compression With Directional Wavelet Transform

The adaptive directional lifting-based wavelet transform (ADL) locally adapts the filtering directions to the local properties of the image. In this letter, instead of using the conventional interpolation filter for the directional prediction with fractional-pel accuracy, a new two-dimensional nonseparable adaptive interpolation filter is proposed. The adaptive filter is calculated for every fractional-pel direction so as to minimize the energy of the prediction error. The tradeoff between reducing the prediction error and the overhead to code the interpolation filter is discussed. This enables coding gains of up to 0.98 dB, compared to ADL coder, and up to 2.4 dB, compared to the JPEG 2000 for typical test images.
Cloud-Based Distributed Image Coding

With multimedia flourishing on the Web, it is easy to find similar images for a query, especially landmark images. Traditional image coding, such as JPEG, cannot exploit correlations with external images. Existing vision-based approaches are able to exploit such correlations by reconstructing from local descriptors but cannot ensure the pixel-level fidelity of the reconstruction. In this paper, a cloud-based distributed image coding (Cloud-DIC) scheme is proposed to exploit external correlations for mobile photo uploading. For each input image, a thumbnail is transmitted to retrieve correlated images and reconstruct it in the cloud by geometrical and illumination registrations. Such a reconstruction serves as the side information (SI) in the Cloud-DIC. The image is then compressed by a transform-domain syndrome coding to correct the disparity between the original image and the SI. Once a bitplane is received in the cloud, an iterative refinement process is performed between the final reconstruction and the SI. Moreover, a joint encoder/decoder mode decision at block, frequency, and bitplane levels is proposed to adapt to different correlations. Experimental results on a landmark image database show that the Cloud-DIC can largely enhance the coding efficiency both subjectively and objectively, with up to 5-dB gains and 70% bits saving over JPEG with arithmetic coding, and perform comparably at low bitrates with the intra coding of the High Efficiency Video Coding standard with a much lower encoder complexity.
Wideband DOA estimation based on sparse representation — An extension of l1-SVD in wideband cases

As a fundamental role in radar signal processing, direction of arrival (DOA) estimation has drawn much attention from researchers. This paper introduces a new wideband DOA estimation model. In this model, instead of processing each subband individually, the whole frequency information of the received signal is combined. And, to utilize the same sparsity among angle spectrums with different subbands, a novel sparsity constraint is proposed. Based on sparse representation framework, an optimization is constructed and solved by second-order cone (SOC) programming to obtain the DOA information. Simulations are provided to prove the superior performance of the proposed method.
Reduced-reference image quality assessment with orientation selectivity based visual pattern

Reduced-reference (RR) image quality assessment (IQA) method aims to accurately measure quality with part of the reference data. The challenge for RR IQA is how to effectively represent the visual content of an image with limited data for quality measurement. Inspired by the orientation selectivity (OS) mechanism in the primary visual cortex, we introduce an OS based visual pattern (OSVP) to extract visual content for RR IQA. The OS arises from the arrangement of the excitatory and inhibitory interactions among connected cortical neurons. Inspired by this, we investigate the correlation among neighbor pixels, and propose the OSVP to represent the visual content of an image. Then, the quality degradation is measured as the changes of OSVP, and a novel RR IQA model is proposed. Experimental results demonstrate that the OSVP based RR IQA model uses limited reference data (9 values) and performs highly consistent with the subjective perception.