The range image has emerged as a dominant representation of 3D LiDAR data, enabling the direct application of well-established image and video compression techniques. However, existing compression methods, primarily optimized for human visual perception, often compromise the fidelity of physical distance information embedded in range images, which is critical for downstream robotic tasks. Additionally, rate-distortion optimization (RDO)-based rate control remains largely unexplored in range image-based LPCC. To address these limitations, we introduce D-Compress, a new framework for detail-preserving, fast, and precise compression of LiDAR range images tailored for real-time streaming. D-Compress focuses on preserving fine-grained range image details while achieving both high compression speed and geometric accuracy. Compared to state-of-the-art (SOTA) codecs, our approach delivers superior geometric precision, high compression ratios, and robust rate control.

Crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) are important tools for researchers seeking to conduct studies with a broad, global participant base. Despite their popularity and demonstrated utility, we present evidence that suggests the integrity of data collected through Amazon MTurk is being threatened by the presence of puppeteers, apparently human workers controlling multiple puppet accounts that are capable of bypassing standard attention checks. If left undetected, puppeteers and their puppets can undermine the integrity of data collected on these platforms. This paper investigates data from two Amazon MTurk studies, finding that a substantial proportion of accounts (33% to 56.4%) are likely puppets. Our findings highlight the importance of adopting multifaceted strategies to ensure data integrity on crowdsourcing platforms. With the goal of detecting this type of fraud, we discuss a set of potential countermeasures for both puppets and bots with varying degrees of sophistication (e.g., employing AI). The problem of single entities (or puppeteers) manually controlling multiple accounts could exist on other crowdsourcing platforms; as such, their detection may be of broader application. While our findings suggest the need to re-evaluate the quality of crowdsourced data, many previous studies likely remain valid, particularly those with robust experimental designs. However, the presence of puppets may have contributed to false null results in some studies, suggesting that unpublished work may be worth revisiting with effective puppet detection strategies.

Differential privacy (DP) has emerged as a promising approach for protecting users' data in the era of big data and machine learning. Despite its deployment by governments and or-ganizations, the concept of DP remains difficult for non-technical users to comprehend. Visual aids, such as infographics, have the potential to bridge this knowledge gap and enable users to make informed data-sharing decisions. In this paper, we propose to use carefully designed infographics to explain DP and compare their effectiveness with traditional text descriptions. We conducted a vignette survey study with 367 participants on Prolific and found that our static and dynamic infographic designs improved participants' understanding of DP, including its mechanism and implication compared with text descriptions. Our infographics also enhance users' understanding of DP and educate them on whether the privacy budget ∊is exposed when sharing their highly sensitive information. This research contributes to the growing body of literature on designing effective DP descriptions to communicate DP to laypeople to facilitate their data-sharing decisions.

In the field of hyperspectral unmixing, deep learning has received increasing attention due to its powerful learning and data representation capabilities. Autoencoder is a popular technique for unmixing. Recently, an autoencoder-based depth image prior algorithm has been proposed for hyperspectral unmixing, which employs geometric methods to extract endmembers. The performance of this network solely focuses on estimating the abundance of images, and it has achieved good unmixing results. However, the depth image only employs one core network, which makes it highly vulnerable to noise and can lead to unstable unmixing outcomes. To address the aforementioned issue, this paper proposes a collaborative consistency autoencoder-based hyperspectral unmixing approach with deep image prior (CCAUDIP). For the proposed CCAUDIP model, it adopts two autoencoders to cooperatively handle the same input data to enhance the generalization ability of the network. Additionally, a consistency constraint is introduced to restrict the abundance outputs of the two core autoencoders and improve the robustness of the network. The experimental results show that the CCAUDIP method can achieve better unmixing results compared to other advanced unmixing algorithms.

Spectral unmixing is a crucial aspect of hyperspectral image processing. Given the low spatial resolution of hyperspectral remote sensing sensors, combined with the complexity and diversity of actual ground objects, hyperspectral remote sensing images often contain numerous mixed pixels, which make spectral unmixing a challenging task. Recent advancements in spectral libraries have shown promising results for decomposing mixed pixels in hyperspectral remote sensing images. Sparse unmixing, a semi-supervised unmixing strategy, avoids the drawbacks of blind source unmixing algorithms, which may extract virtual endmembers with no physical meaning. In this paper, we propose the multiscale spatial sparse unmixing (MSSU) algorithm, which utilizes the signal adaptive spatial multiscale unmixing of the over-segmentation method to decompose the complex unmixing problem. Furthermore, weighting factors are introduced to extract spatial information from the spectral image. The experimental results obtained from simulated hyperspectral datasets reveal the great potential of the proposed algorithm in unmixing.

Integrated Sensing and Communication (ISAC) is an emerging technology that integrates wireless sensing and communication into a single system, transforming many applications, including cooperative mobile robotics. However, in scenarios where radio communications are unavailable, alternative approaches are needed. In this paper, we propose a new optical ISAC (OISAC) scheme for cooperative mobile robots by integrating camera sensing and screen-camera communication (SCC). Unlike previous throughput-oriented SCC designs that work with stationary SCC links, our OISAC scheme is designed for real-time control of mobile robots. It addresses new challenges such as image blur and long image display delay. As a case study, we consider the leader-follower formation control problem, an essential part of cooperative mobile robotics. The proposed OISAC scheme enables the follower robot to simultaneously acquire the information shared by the leader and sense the relative pose to the leader using only RGB images captured by its onboard camera. We then design a new control law that can leverage all the information acquired by the camera to achieve stable and accurate formations. We design and conduct real-world experiments involving uniform and nonuniform motions to evaluate the proposed system and demonstrate the advantages of applying OISAC over a benchmark approach that uses extended Kalman filtering (EKF) to estimate the leader's states. Our results show that the proposed OISAC-augmented leader-follower formation system achieves better performance in terms of accuracy, stability, and robustness.

As the spectral library continues to expand, sparse hyperspectral unmixing methods have been developed to solve the mixing problem without the need for end-member extraction or generation. These methods leverage the intrinsic spectral and spatial information to enhance the accuracy of fractional abundance estimation. However, their effectiveness is limited by rigid spatial regularization and insufficient utilization of spectral spatial information, which hampers the improvement of unmixing performance. To overcome this limitation, we present a novel algorithm named Local Spectral Similarity Guided Sparse Hyperspectral Unmixing with Spatial Graph Regularization (SGSU). In SGSU, we introduce a spatial graph regularization to enforce the inter-pixel correlation within spatial clusters and assign them to corresponding abundance vectors. To reduce the computational cost, we employ an adaptive superpixel-based spatial grouping strategy to segment the hyperspectral image, which translates the intrinsic geometry into constraints on abundance. Furthermore, we introduce a weighting factor with two components into the sparse unmixing framework. One component is based on the row sparsity of the estimated abundances, indicating the presence of active end-members; the other component is based on the similarity between neighboring pixels, which promotes piecewise smoothness of the estimated abundances. To obtain a more robust solution, we adopt a double-loop scheme based on the alternating direction method of multipliers (ADMM) algorithm to solve the SGSU model. Experimental results on both simulated and real hyperspectral datasets demonstrate that the proposed SGSU algorithm outperforms state-of-the-art sparse unmixing methods in terms of both accuracy of abundance estimation and end-member identification from spectral libraries. Our algorithm achieves superior unmixing results, which indicates its potential for practical applications in hyperspectral imaging.

Sparse unmixing is a semi-supervised method whose pur-pose is to find the best subset of library entries from the spec-tral library that best model the image. In sparse unmixing, the current main development direction is to incorporate the spatial information of the image into the model. Existing spa-tial sparse unmixing algorithms mainly use spatial weights or spatial regularization to characterize the spatial correlation between pixels to improve the unmixing results. For the complex and diverse hyperspectral data in reality, most al-gorithms are only good at processing a single scene, which brings greater challenges to their practicality. In order to ad-dress this issue, a new dual spatial weighted sparse unmixing model (DSWSU) is proposed, which simultaneously ex-ploits the spatially homogeneous information of images. For the proposed DSWSU, a pre-calculated superpixel weighting factor is designed to mitigate the effect of noise on unmixing. Meanwhile, the spatial neighborhood weighting factor aims to promote the local smoothness of the abundance maps. As a simple unmixing model, the proposed DSWSU can be quickly solved by the alternating direction multiplier method (ADMM). Experimental results on simulated hyperspectral data indicate that the proposed DSWSU method can achieve accurate abundance estimation in various scenarios (low or high noise interference), and obtain better unmixing results than other state-of-the-art unmixing algorithms.

Spectral unmixing is an essential technology for the interpretation of hyperspectral remote sensing images. Sparse unmixing has become a research hotspot in the field of spectral unmixing since it circumvents the issue of endmember extraction. Regularization based on spatial information further improves the performance of sparse unmixing. However, multiple regularization terms increase the complexity of the sparse model and the difficulty of regularization parameter tuning. To overcome this drawback, a new dual reweighted low-rank and total variation sparse unmixing (DRLRSU-TV) method is proposed, which jointly imposes the low-rank constraint, dual reweighted sparse constraint and total variation (TV) regularizer on the classic sparse unmixing model via two regularization terms. Experiment results on simulated hyper-spectral data verify the excellent unmixing performance of the proposed algorithm compared to other state-of-the-art sparse unmixing methods.

In the field of hyperspectral unmixing (HU), deep learning (DL) techniques have attracted increasing attention due to their powerful capabilities in learning and feature extraction. The autoencoder framework has flexible scalability as well as good unsupervised learning ability, and it achieves good performance in hyperspectral unmixing. However, some traditional autoencoder-based algorithms only focus on pixel-level reconstruction loss, which ignores the detailed information contained in the material. In addition, these algorithms usually only use a single autoencoder with non-convex properties, which brings great difficulty to the solution. In this paper, a cascaded autoencoders-based spectral-spatial unmixing (CASSU) framework is proposed to address these issues. For the proposed CASSU, on the one hand, two concatenated autoencoders are introduced to better find the global optimal solution. On the other hand, in this cascaded deep network, spectral angle mapping (SAM) and convolution operations are used to extract the spectral-spatial information of the image. Experimental results on real hyperspectral data indicate that the newly proposed CASSU algorithm has better unmixing performance compared to several state-of-the-art unmixing algorithms.

Hyperspectral unmixing is an important image interpre-tation technique that aims to estimate the pure constituent materials (endmembers) and their corresponding fractional abundances in each mixed pixel. Nonnegative matrix factorization (NMF) has attracted a lot of attention because of its ability to solve mixed pixel scenarios. The sparse NMF method achieves better unmixing results thanks to its full use of the sparse characteristics of the data. However, most existing sparse NMF unmixing techniques lack the consid-eration of spatial information. In fact, hyperspectral images contain intrinsic geometric information as well as rich spatial information. In this paper, a spatial graph regularized nonneg-ative matrix factorization unmixing framework (SGNMF) is established. For the proposed SGNMF, on the one hand, the graph regularization is introduced to characterize the latent manifold structure of the data, and on the other hand, the spatial weighting factor is used to mine the spatial correlation between pixels. The optimization problem of the SGNMF model can be solved by a multiplicative iterative rule. Exper-imental results on synthetic data sets indicate that the newly proposed SGNMF method is able to produce better results than other advanced spectral unmixing algorithms.

Remotely sensed hyperspectral images contain several bands (at about adjoining frequencies) for a similar zone on the surface of the Earth. Hyperspectral unmixing is a significant method for breaking down hyperspectral images into the components (endmembers) that conform each (potentially mixed) pixel and their abundance maps. Nonnegative matrix factorization (NMF) has attracted huge consideration because of the way that it can address mixed pixel scenarios. Most existing NMF unmixing techniques do not include spatial information in the analysis. An ongoing trend is to fuse the spatial and the spectral information contained in hyperspectral scenes to improve the solution. In this article, we build up another hyperspectral unmixing technique named spectral–spatial weighted sparse NMF (SSWNMF), in which two weighting factors are acquainted into the NMF model to upgrade the sparsity of the solution and capture the piecewise smooth structure of the data. We adopt a multiplicative iterative strategy to implement the proposed SSWNMF model. Our experimental results, conducted with both synthetic and real hyperspectral data, uncover that the proposed SSWNMF strategy can get accurate unmixing results over those gave by other unmixing strategies, with less parameter tuning.

With the increase of available spectral libraries, sparse unmixing has attracted great attention in the field of hyperspectral image unmixing. When the spatial information is integrated into the traditional sparse unmixing model, it achieves better performance. However, the less accurate description of the spatial structure limits the performance of the previous spatial sparse unmixing methods. To address this limitation, a new technique called superpixel based low-rank sparse unmixing (SpLRSU) is established, which encourages the local spatial consistency and the spatial continuity of the image. Specifically, superpixel segmentation is used to adaptively generate local homogeneous regions, and then the low-rank constraint is enforced on the abundance vectors of each spatial group to preserve the low-dimensional structure of superpixel blocks. Meanwhile, the spectral-spatial weighted sparse regularization term is introduced to promote the sparsity of fractional abundances in the spectral and spatial domains. The experimental results on the synthetic data set show that the newly proposed algorithm is superior to other advanced sparse unmixing algorithms.

Spectral unmixing is an important technique for hyperspectral image application, which aims to estimate the pure spectral signatures in each mixed pixel and their corresponding fractional abundances. However, due to the influence of factors such as illumination, topography change and atmosphere, spectral variability is inevitable, which will lead to inaccurate unmixing results. Traditional unmixing methods fail to handle this problem, especially the complex spectral variability in the image. To address this limitation, a new technique called low-rank subspace unmixing (LRSU) was established, which aims to jointly estimate a subspace projection and abundance maps. For the proposed LRSU approach, the original data is projected into a low-rank subspace to deal with various spectral variabilities in spectral unmixing. Meanwhile, the spectral-spatial weighted sparse regularization term is introduced to upgrade the sparsity of the solution and capture the piecewise smooth structure of the data. The experimental results, conducted using synthetic data sets, quantitatively indicate that the proposed LRSU strategy produces better results than other advanced spectral unmixing methods.

Hyperspectral unmixing aims to decompose a hyperspectral image (HSI) into a collection of constituent materials, or end-members, and their corresponding abundance fractions. Recently, nonnegative tensor factorization (NTF)-based spectral unmixing methods have attracted significant attention owing to their outstanding performance when representing an HSI without any information loss. However, tensor factorization-based HSI methods do not fully exploit the spatial contextual information present in the scene. Besides, these approaches are sensitive to low signal-to-noise ratio (SNR) in HSIs. To address this limitation, we propose a new spectral-spatial weighted sparse nonnegative tensor factorization (SSWNTF) method to preserve the spatial details in the abundance maps via the spectral and spatial weighting factors. Our experiments with simulated data sets certified that the proposed method outperforms other advanced methods.

Sparse representation-based approaches have been successfully applied to remotely sensed hyperspectral image unmixing. In recent years, sparse unmixing techniques have incorporated spatial information into the sparse unmixing model, achieving improved fractional abundance results. Most spatial-based sparse unmixing methods utilize regular-shaped neighborhoods (e.g., a cross or a square window) to characterize the spatial-contextual information around each pixel. However, the spatial characteristics of natural scenes are not always uniform, but vary according to the observed objects. Therefore, assuming uniform spatial neighborhoods may not be consistent with real spatial structures in the scene. Super-pixels offer a good solution to this problem since they can better characterize such spatial structures. Based on this observation, in this paper we develop a new superpixel-guided sparse unmixing (SPGSU) method for hyperspectral scenes. The proposed SPGSU includes the spatial correlation through a superpixel-based technique rather than assuming predefined pixel grids. Each superpixel can be regarded as a small spatial region, whose shape and size can be adaptively changed to accommodate different spatial structures. Our experimental results, conducted using simulated data sets, quantitatively indicate that our newly proposed method produces better results than other advanced spectral unmixing methods.

This paper address issues that arise in copyright protection systems of digital images, which employ blind watermark verification structures in the curvelet domain. First, we observe that statistical distribution with heavy algebraic tails, such as the alpha-stable family, are in many cases more accurate modeling tools for the curvelet coefficients than families with exponential tails such as generalized Gaussian. Motivated by our modeling results, we then design a new processor for blind watermark detection using the Cauchy member of the alpha-stable family. We analyze the performance of the new detector in terms of the associated probabilities of detection and false alarm and we compare it to the performance of the generalized Gaussian detector and the traditional correlation-based detector by performance experiments. The experiments prove that Cauchy detector is superior to the others.

One of the problems encountered in image transmission is the cut-off or error of a bits chain at the moment of transmission. To protect the transmitted signal, it is necessary to couple quantized transform coding with a redundant transform. Ridgelet transform is a new directional resolution transform and it is more suitable for describing the signals with line or super-plane singularities. Finite ridgelet transform is a discrete orthonormal version of ridgelet transform proposed by Minh N.Do and Martin Vetterli. In this paper, we propose a new conception of redundant ridgelet transform, which we implement it mainly by controlling the ratio of the redundancy at the step of Radon transform in the ridgelet transform. We first briefly introduce the concept of ridgelet transform. Then, we illustrate finite ridgelet transform and the new method. Finally, the main features of the redundant ridgelet transform and its applications for image coding and image analysis are discussed.