Snapshot compressive imaging (SCI) and video compressive sensing (VCS) typically use fixed, globally uniform compression ratios that ignore spatio-temporal heterogeneity. We present D-STCRL, a reinforcement-learned framework that unifies adaptive sensing and semantic transmission under an explicit rate-distortion-energy objective. The pipeline comprises: (i) a Ratio Generation Network predicts per-patch ratio maps via spatio-temporal attention and 3D frequency cues; (ii) a Programmable Sensing Model emulates pixel-wise variable exposure through differentiable binary gating under a global budget; and (iii) a Frequency-Aware Swin decoder with a low-rank prior restores temporally consistent frames. A multi-objective policy gradient couples the ratio policy with reconstruction and JSCC, yielding stable training. On the NFS benchmark, D-STCRL improves PSNR by$2-3 ~\text{dB}$over fixed-ratio SCI at the same sampling budget; under 10 dB AWGN it surpasses a CRL baseline by$0.6-1.0 ~\text{dB}$while reducing transmitted symbols by up to 15 %. These results unify content-adaptive sensing and efficient transmission for next-generation cameras. Our code, configs and reproducible pipelines will be released upon acceptance.

Existing image compression models often lack personalization capabilities, treating all image regions equally and failing to meet the compression needs of different users for specific Regions of Interest (ROI). To address this challenge, we propose an innovative variable rate image compression framework that achieves user-centric dynamic compression by introducing visual in-context learning. Our method extracts cross-image semantics from user-provided visual examples to understand their intent. This semantic information is then converted into visual semantic query tokens and spatial masks to effectively guide the bit allocation of the compression model. Furthermore, we design a novel Semantic Spatial Control Block (SSCB) to fully leverage these semantic and spatial cues, thereby achieving a balance between preserving user-specified details and overall image quality. Experimental results demonstrate that our method significantly improves performance on the ROI, achieving a 31.54 % BD-Rate reduction and a 2.7479 dB BD-PSNR gain over the baseline model.

3D Gaussian Splatting (3DGS) has emerged as an efficient explicit representation for real-time novel view synthesis. However, repeated densification during training often leads to uncontrolled Gaussian growth, causing heavy memory overhead and slow convergence. This redundancy stems from two limitations: (1) opacity-based sparsification that fails to maintain consistent pruning pressure under evolving distributions; (2) irreversible pruning strategies that risk eliminating essential Gaussians. To address these, we proposeAdaptive and Recoverable Gaussian Pruning (ARGP), a training-integrated framework that suppresses redundant growth while preserving reconstruction quality. During densification, we employAdaptive Opacity Pruning (AOP), which removes a fixed proportion of low-opacity Gaussians based on quantiles, effectively suppressing redundancy. In the fine-tuning stage, we introduceIterative Recovery Pruning (IRP), which selectively reinstates critical Gaussians using a gradient-informed recovery score, thus preventing over-pruning and preserving reconstruction quality. Extensive experimental results on Mip-NeRF 360, Tanks & Temples, and Deep Blending validate the effectiveness of our proposed ARGP. For instance, ARGP achieves a 90.2% reduction in Gaussian counts and a 1.33× training speedup, while maintaining competitive reconstruction quality on Mip-NeRF 360. Our code is available at https://github.com/Sinyo-Liu/ARGP.

In this paper, we propose a generative face video compression framework based on affine transformations to better represent large movements without parameter transmission. It mainly consists of an encoder and decoder, and our encoder is similar to the one in [1]. Intra frame are compressed by the existing encoder, while subsequent inter frames are compressed into compact inter frame features. In the decoder, feature alignment is first established to map the decoded intra frame and inter frame features into the same domain. The aligned features are then combined with the appearance features extracted by the appearance encoder from the intra frame and fed into the coarse-fine affine transform module to establish motion estimation and compensation. The coarse affine transform focuses on global motion, while the fine affine transform deals with local motion, such as lip motion. Finally, the transformed features are fed into the image generation module to obtain the final reconstruction results.

The limited bandwidth of 6G networks presents a significant challenge in accommodating the growing number of user connections. To tackle this issue, we propose a novel multi-user access paradigm for SemCom, termed non-orthogonal code-words for semantic communication (NOC4SC). By leveraging the Swin Transformer, NOC4SC enables users to independently extract their semantic features while maintaining network parameter sharing. NOC4SC ensures that the user’s data remains protected from unauthorized decoding while allowing simultaneous multi-user transmissions over shared time-frequency resources without the need for spread spectrum. Specifically, we introduce an adaptive NOC and SNR Modulation (NSM) block, which employs deep learning techniques to adaptively regulate the SNR mechanism. The NSM block facilitates the generation of approximately orthogonal semantic features across separable feature subspaces, thereby mitigating inter-user interference. Extensive experimental results demonstrate the superiority of NOC4SC, achieving a 5.02 dB improvement in PSNR at low SNR and 2.12dB at high SNR in the Rayleigh fading channel.

Diffusion models excel at audio-driven gesture generation. However, the reverse denoising process is computationally intensive and time-redundant. Additionally, existing methods predominantly focus on the quality and diversity of upper-body gestures, without considering the movement trajectories of speaker. To address these issues, we introduce, for the first time, a Latent Consistency Model for co-speech gesture generation (GestureLCM), which allows for large-scale skip sampling while ensuring consistency between adjacent perturbations in the latent probability flow space. Moreover, we integrate ControlNet into the latent space of GestureLCM to explicitly control the trajectory generation using spatial signals. By employing this approach, our model can generate content-related movements. Extensive experiments show the efficiency and controllability of our method for audio-driven co-speech gesture generation, achieving 50× acceleration in inference speed.

Given a complex text description, existing studies have indeed made significant progress in motion synthesis, which largely overlook the fine-grained action information from expressive texts. In this paper, we introduce LGDDM, a LLM-Guided Dual-branch Diffusion Model, which relies on the LLM’s ability to decompose text and provide fine-grained and interpretable guidance to generate motions. To ensure the coordination and coherence of target motions, we then design an attention-structured dual-branch fusion block, which adaptively balances high-fidelity and diverse motion synthesis. Furthermore, we develop a large-scale decomposed human motion dataset to justify the effectiveness of our approach. Extensive experiments prove that combined with long text description, decomposed action information is rather important and essential for fine-grained human motion synthesis.

Although diffusion models advance condition-based visual generation, they suffer from speed and cost issues, unlike faster AutoRegressive methods that are limited in performance. To address these, we introduce the Stable Control Visual AutoRegressive Model (SCVAR). SCVAR ensures stable control by aligning visual conditions on multiple scales. Rather than unfolding the 2D image into a 1D raster, SCVAR decouples it into multiple scales. This shifts the sequential representation in SCVAR from tokens to scales, satisfying the unidirectional dependency of the AR model while preserving the 2D structure of the image. Compared to indiscriminate conditional guidance, cross-scale alignment provides more precise constraints, enabling SCVAR to achieve state-of-the-art performance in experiments against diffusion models, with 10x faster generation speed. The decoupled condition also reduces training costs. Compared to end-to-end conditional computation, experiments demonstrate that SCVAR matches performance with only 40% additional parameters.

In recent years, Text-to-Image (T2I) models have made remarkable advancements, yet accurate accurate association of attributes remains a key challenge. This paper presents FreeAlign, a novel training-free framework designed to enhance attribute alignment in T2I generation. By modulating attention and adapting U-Net components, FreeAlign achieves precise alignment between image attributes and textual descriptions. It strengthens attribute-target associations through refining attention maps, adjusts U-Net’s backbone and skip connections based on energy ratios, and reorders prompts to balance attribute focus. Large Language Models (LLMs) enrich prompts with diverse, contextually relevant text, enhancing diffusion models’ generative power and quality. Extensive experiments show that FreeAlign delivers superior alignment for diverse prompts while preserving intricate details and ensuring structural integrity, establishing a new benchmark for attribute precision in T2I generation.

Unlike traditional bit-level data transmission methods, semantic communication focuses on conveying the meaning behind the data. Though promising results have been achieved, existing end-to-end learning-based semantic communication frameworks often require a synchronization of deep models between the transmitter and the receiver. Such design leads to tens of thousands models to be stored at receiver since different manufactures may optimize their own models. To address this problem, we propose a novel model-unaware generative image compression framework for semantic communication. It features at employing human-understandable multi-modality representations as an intermediate layer to enhance information transmission efficiency and semantic consistency. Our framework introduces a mask-based rate-distortion optimization module, which effectively removes low-relevance information for image generation and reduces the bit rate while maintaining semantic consistency. Experimental results demonstrate that the framework can still reconstruct high-quality images at very low bit rates, showcasing its potential for applications in modern communication systems.

In image semantic communication, the granularity of semantic descriptions required for different objects within an image varies based on the communication intent and the importance of the objects. However, current semantic codecs optimized for global assessment metrics fail to adapt to user intent and cannot provide differentiated semantic granularity for objects of different importance. Generative semantic codecs using representations such as edge maps or semantic segmentation maps are insufficient for capturing fine-grained semantic information. This paper proposes dividing the transmitted image semantics into global coarse-grained and key object fine-grained semantics to better align with sender intent and optimize bandwidth usage. We introduce a novel semantic codec scheme based on a pre-trained text-to-image diffusion model. Global coarse-grained semantics are represented using short textual descriptions. Fine-grained semantic information of key objects is extracted using the Denoising Diffusion Implicit Model (DDIM) inversion and compressed in the frequency domain. Experimental results demonstrate that the proposed semantic codec enables high-quality recovery of coarse- and fine-grained semantics in image transmission while significantly reducing data transmission requirements.

In this paper, we propose a novel task of 3D Human Motion Moment Retrieval (HMMR), which aims to find the motion segments from a large motion corpus that semantically correspond to a given linguistic query. To better understand this goal, we treat this unexplored task as a combination of motion retrieval (MR) and moment localization (ML) subtasks. However, most existing retrieval methods directly align text and motion through contrastive learning (for MR), often overlooking the contextual richness and semantic overlap between different moments (for ML). Inspired by this, we propose a Multi-Granularity Semantic-aware Network (MGSNet) to perceive the target moment from multiple atomic motions with intricate semantics. Specifically, (1) a query-aligned attention module is constructed to learn the semantic representations of different modalities; (2) a moment match module is then introduced to comprehensively align text and motion sequence by decomposing the motion into three granularities: frame-, segment- and event-wise levels. Extensive experiments on HumanML3D-Aug and BABEL datasets demonstrate the potential of our framework in corpus-level HMMR.

Coded Aperture Snapshot Spectral Imaging (CASSI) multiplexes 3D Hyperspectral Images (HSIs) into a 2D sensor to capture dynamic spectral scenes, which, however, sacrifices the spatial information. Dual-Camera Compressive Hyperspectral Imaging (DCCHI) enhances CASSI by incorporating a Panchromatic (PAN) camera to compensate for the loss of spatial information in CASSI. However, the dual-camera structure of DCCHI disrupts the diagonal property of the product of the sensing matrix and its transpose, making it difficult to efficiently and accurately solve the data subproblem in closed-form and thereby hindering the application of model-based methods and Deep Unfolding Networks (DUNs) that rely on such a closed-form solution. To address this issue, we propose an Alternating Direction DUN, named ADRNN, which decouples the imaging model of DCCHI into a CASSI subproblem and a PAN subproblem. The ADRNN alternately solves data terms analytically and a joint prior term in these subproblems. Additionally, we propose a Cross Spectral Transformer (XST) to exploit the joint prior. The XST utilizes cross spectral attention to exploit the correlation between the compressed HSI and the PAN image, and incorporates Grouped-Query Attention (GQA) to alleviate the burden of parameters and computational cost brought by impartially treating the compressed HSI and the PAN image. Furthermore, we built a real DCCHI system and captured large-scale indoor and outdoor scenes for future academic research. Extensive experiments on both simulation and real datasets demonstrate that the proposed method achieves state-of-the-art (SOTA) performance. The code and datasets have been open-sourced at: https://github.com/ShawnDong98/ADRNN-XST.

Monitoring multimodal signals provides a more comprehensive understanding of health conditions compared to singlemode monitoring. In the face of the significant volumes of multimodal signals, existing IoT health monitoring systems primarily focus on high-fidelity signal transmission by encoding multimodal signals separately. However, due to the lack of consideration for the downstream applications and correlation between multimodal signals, a portion of bandwidth resources is wasted on task-irrelevant information and intermodal redundancy. To address this issue, we propose the Multimodal Semantic Integration Communication (MoSIC) framework composed of three levels: At the sensor level, multiple wearable sensors collect and send different modal signals to a mobile terminal; at the mobile terminal level, the terminal employs deep source-channel joint encoding for the received multimodal signals, extracting single-modal embedded features using a backbone network, and obtaining cross-modal features through a feature fusion network with contrastive constrain; at the cloud level, a decoding network symmetric to the encoding network reconstructs the multimodal signals, which are then used for downstream applications such as human activity recognition. MoSIC focuses on semantically integrating multimodal signals for downstream applications, resulting in improved encoding and transmission efficiency. It also reduces the radio-frequency power consumption and bandwidth requirements.

In recent years, semantic communication based on deep learning for source-channel joint encoding has garnered significant attention. It utilizes network models trained end-to-end to represent signals as embedding vectors and has demonstrated superior performance compared to traditional methods. However, due to the significant disparity between embedding vectors and human language, it can be challenging to succinctly capture abstract semantics such as scenes. In this paper, we introduce the Scene Graph-based Generative Semantic Communication (SG2SC) framework, built upon structured semantics and conditional generative models for image transmission. SG2SC aims to faithfully convey abstract semantics like scenes. It begins by detecting object categories, spatial attributes, and inter-category relationships in the image, representing scene semantics in a graph structure. Subsequently, it employs graph neural networks for scene graph encoding, decoding, and transmission, and finally utilizes a conditional diffusion model for semantic decoding. Benefiting from its concise graph structure semantics, SG2SC outperforms traditional method, semantic communication based on deep joint source-channel coding, and segmentation-based generative semantic communication in terms of noise resistance and encoding efficiency.

In the coded aperture snapshot spectral imaging (CASSI) system, deep unfolding networks (DUNs) have demonstrated excellent performance in recovering 3-D hyperspectral images (HSIs) from 2-D measurements. However, some noticeable gaps exist between the imaging model used in DUNs and the real CASSI imaging process, such as the sensing error as well as photon and dark current noise, compromising the accuracy of solving the data subproblem and the prior subproblem in DUNs. To address this issue, we propose a degradation estimation network (DEN) to correct the imaging model used in DUNs by simultaneously estimating the sensing error and the noise level, thereby improving the performance of DUNs. Additionally, we propose an efficient local and non-local transformer (LNLT) to solve the prior subproblem, which not only effectively models local and non-local similarities but also reduces the computational cost of the window-based global multihead self-attention (MSA). Furthermore, we transform the DUN into a recurrent neural network (RNN) by sharing parameters of DNNs across stages, which not only allows DNN to be trained more adequately but also significantly reduces the number of parameters. The proposed DERNN-LNLT achieves state-of-the-art (SOTA) performance with fewer parameters on both simulation and real datasets (code:https://github.com/ShawnDong98/DERNN-LNLT).

Real-time electrocardiogram (ECG) monitoring and diagnosis through Internet of Things (IoT) are crucial for addressing the severity and timely treatment of cardiovascular diseases, enabling timely intervention and preventing life-threatening complications. However, current ECG monitoring research predominantly focuses on individual aspects such as signal compression, diagnostic analysis, or secure transmission, lacking joint optimization of various modules in IoT scenarios. To address this gap, this work proposes a novel framework based on superimposed semantic communication for real-time ECG monitoring in IoT. The framework comprises three hierarchical levels: the edge level for data collection and processing, the relay level for signal compression and coding, and the cloud level for data analysis and reconstruction. The proposed framework offers several unique advantages. By employing semantic encoding guided by ECG classification tasks, it selectively extracts crucial features within and between signals, improving compression ratio and adaptability to channel noise. The superimposed semantic encoding achieves content encryption without requiring any additional operations. Moreover, the framework utilizes lightweight anomaly detection neural networks, reducing edge device power consumption and conserving communication resources. Simulation and real experimental results demonstrate that the proposed method achieves real-time encoding and transmission of ECG signals with a compression ratio of 0.019 on the MIT-BIH dataset. Furthermore, it attains a heartbeat classification accuracy of 0.988 and a reconstruction error of 0.061.

Single-user semantic communications have attracted extensive research recently, but multi-user semantic broadcast communication (BC) is still in its infancy. In this paper, we propose a practical robust features-disentangled multi-user semantic BC framework, where the transmitter includes a feature selection module and each user has a feature completion module. Instead of broadcasting all extracted features, the semantic encoder extracts the disentangled semantic features, and then only the users’ intended semantic features are selected for broadcasting, which can further improve the transmission efficiency. Within this framework, we further investigate two information-theoretic metrics, including the ultimate compression rate under both the distortion and perception constraints, and the achievable rate region of the semantic BC. Furthermore, to realize the proposed semantic BC framework, we design a lightweight robust semantic BC network by exploiting a supervised autoencoder (AE), which can controllably disentangle sematic features. Moreover, we design the first hardware proof-of-concept prototype of the semantic BC network, where the proposed semantic BC network can be implemented in real time. Simulations and experiments demonstrate that the proposed robust semantic BC network can significantly improve transmission efficiency.

To acquire a snapshot spectral image, coded aperture snapshot spectral imaging (CASSI) is proposed. A core problem of the CASSI system is to recover the reliable and fine underlying 3D spectral cube from the 2D measurement. By alternately solving a data subproblem and a prior subproblem, deep unfolding methods achieve good performance. However, in the data subproblem, the used sensing matrix is ill-suited for the real degradation process due to the device errors caused by phase aberration, distortion; in the prior subproblem, it is important to design a suitable model to jointly exploit both spatial and spectral priors. In this paper, we propose a Residual Degradation Learning Unfolding Framework (RDLUF), which bridges the gap between the sensing matrix and the degradation process. Moreover, a MixS2Transformer is designed via mixing priors across spectral and spatial to strengthen the spectral-spatial representation capability. Finally, plugging the MixS2Transformer into the RDLUF leads to an end-to-end trainable neural network RDLUF-MixS2. Experimental results establish the superior performance of the proposed method over existing ones. Code is available: https://github.com/ShawnDong98/RDLUF_MixS2

Semantic communications utilize the transceiver computing resources to alleviate scarce transmission resources, such as bandwidth and energy. Although the conventional deep learning (DL) based designs may achieve certain transmission efficiency, the uninterpretability issue of extracted features is the major challenge in the development of semantic communications. In this paper, we propose an explainable and robust semantic communication framework by incorporating the well-established bit-level communication system, which not only extracts and disentangles features into independent and semantically interpretable features, but also only selects task-relevant features for transmission, instead of all extracted features. Based on this framework, we derive the optimal input for rate-distortion-perception theory, and derive both lower and upper bounds on the semantic channel capacity. Furthermore, based on the$\beta $-variational autoencoder ($\beta $-VAE), we propose a practical explainable semantic communication system design, which simultaneously achieves semantic features selection and is robust against semantic channel noise. We further design a real-time wireless mobile semantic communication proof-of-concept prototype. Our simulations and experiments demonstrate that our proposed explainable semantic communications system can significantly improve transmission efficiency, and also verify the effectiveness of our proposed robust semantic transmission scheme.

In hyperspectral image (HSI) classification, it is a great challenge on how to extract key informative spectral–spatial features efficiently and suppress useless features from abundant spectral–spatial information. In this article, inspired by the attention mechanism of the human visual system, we propose a novel spectral–spatial–semantic network (S3Net) with the multiway attention mechanism for HSI classification. The S3Net consists of a spectral branch, a spatial branch, and a multiscale semantic module. The spectral branch extracts the multiway spectral features with a dense spectral block and a multiway spectral attention module. The spatial branch extracts the multiway spatial features with a multiscale spatial block and a multiway spatial attention module. The multiscale semantic module extracts spectral–spatial–semantic features that are used for classification. In the proposed S3Net, the multiway attention modules in spectral and spatial branches are built to enhance the extraction ability of the key informative spectral–spatial features. The multiscale spatial block in the spatial branch is designed to learn strong complementary and related information. The Res2Net in the multiscale semantic module is used to learn multiscale semantic features at a granular level. A large number of experimental results demonstrate that, on the University of Pavia, Kennedy Space Center, and Pavia Center data sets, the proposed S3Net achieves higher classification accuracy than state-of-the-art methods on the limited training samples. Remarkably, our S3Net also achieves the best performance on the mineral exploration HSI data set, called Huoshaoyun, which is collected by the GaoFen-5 (GF-5) satellite.

It is a challenge to acquire a snapshot image of very high resolutions in both spectral and spatial domain via a single short exposure. In this setting one cannot trade time for spectral resolution, such as via spectral bands scanning. Cameras of color filter arrays (CFA) (e.g., the Bayer mosaic) cannot obtain high spectral resolution. To overcome these difficulties, we propose a new multispectral imaging system that makes random linear broadband measurements of the spectrum via a nanostructured multispectral filter array (MSFA). These MS-FA random measurements can be used by sparsity-based recovery algorithms to achieve much higher spectral resolution than conventional CFA cameras, without sacrificing spatial resolution. The key innovation is to jointly exploit both spatial and spectral sparsity properties that are inherent to spectral reflectance of natural objects. Experimental results establish the superior performance of the proposed multispectral imaging system over existing ones.

Zero-shot learning (ZSL), which focuses on transferring the knowledge from the seen classes to unseen ones, has attracted more and more attention in the computer vision community. Exploring the relationships among the spaces of visual representation, semantic description and label information is a key to the success of ZSL. In this paper, we propose a novel approach by using a two-layer Stacked AutoEncoder (StAE) with manifold regularizations to construct the tight relations of different spaces, where the first-layer encoder aims to project a visual feature vector into the semantic space, and the second-layer encoder connects the semantic description of a sample with its label directly. Meanwhile, the decoders seek to reconstruct the visual representation from label information and semantic description successively. Besides, two manifold regularizers are integrated in the stacked autoencoder, which captures the manifold structures residing in the different spaces effectively. Compared with the previous related works, the proposed approach is a more general framework and has stronger transfer ability from seen classes to unseen classes. Extensive experiments on the benchmark datasets clearly demonstrate that our StAE performs significantly better than the state-of-the-arts.

Existing low-level vision algorithms (e.g., those for superresolution, denoising, deblurring etc.) were primarily motivated and optimized for precision in spatial domain. However, high precision in spectral domain is of importance for many applications in scientific and technical fields, such as spectral analysis, recognition, and classification. In quest for both high spectral and spatial fidelity we introduce previously-unexplored, physically-induced, joint spatiospectral sparsities to improve existing methods for multispectral image restoration. The bidirectional image formation model is used to reveal that the discontinuities of a multispectral image tend to align spatially across different spectral bands; in other words, the 2D Laplacians of different bands are not only sparse each, but they also agree with one the other in significance positions. Such strongly structured sparsities give rise to a new inter-and intra-block sparse estimation approach. The estimation is performed on 3D spatiospectral sample blocks, rather than on separate 2D patches, one per spectral band or per luminance and chrominance component as in current practice. Moreover, intra-block and inter-block sparsity priors are combined via an intra-block ℓ1,2-norm minimization term and an inter-block low rank term, strengthening the regularization of the underlying inverse problem. The new approach is tested and evaluated on two concrete applications: superresolving and denoising multispectral images; its validity and advantages over the current state of the art are established by empirical results.

In quest for high spectral fidelity in spatial superresolution of multispectral images, we explore physically-induced, joint spectral-spatial sparsities. The bichromatic image formation model is used to reveal that the discontinuities of a multi-spectral image tend to align spatially across different spectral bands; in other words, the 2D Laplacians of different bands are not only sparse but also agree with one the other in positions of significance. This strong prior of natural images can be incorporated, as an ℓ1,2-norm regularization term, into an inverse problem formulation for superresolution of multispectral images. Experiments show that exploiting the newly discovered joint spectral-spatial sparsities can improve the performance of existing methods, especially in spectral fidelity.

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

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.

Color demosaicking is an ill-posed inverse problem of image restoration. The performance of a color demosaicking algorithm depends on how thoroughly it can exploit domain knowledge to confine the solution space for the underlying true color image. We propose a sparsity-based ℓ1minimization technique for color demosaicking that exploits both interband and intra-band sparse representations of natural images. In some of most challenging cases of color demosaicking, the proposed technique outperforms those published in the literature by a significant margin in both PSNR and visual quality.

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