Image dehazing is a crucial low-level restoration task that aims to recover a clear image from a hazy observation. Recent learning-based approaches have demonstrated impressive performance in this area. However, there are still two drawbacks: (1) Existing dehazing architectures do not sufficiently consider non-uniform haze distribution, indicating that the haze location information is underexplored. (2) Naïve contrastive regularization fails to provide enough constraint force in solution space , i.e., negative-oriented supervision information cannot be fully utilized during the training stage. Consequently, we establish a Multi-stage Dehazing Network (MSD-Net) to achieve single image haze removal. For one thing, we build a haze perception unit (HPU) based on a self-calibration attentive paradigm. This unit can effectively encode haze location information as prior guidance and further enhance its feature representation capabilities. For another, we tailor a global and local progressive contrastive regularization (GLPCR) to explore negative-oriented supervision information. Specifically, the negative samples are derived not only from the original hazy images but are also progressively updated through the pseudo-restoration results of the multi-stage architecture. To tackle the learning ambiguity arising from diverse negative samples, we employ a curriculum learning strategy during the training phase. Moreover, our GLPCR operates in both global and local manners, encouraging the network to retain rich information from both image-wise and patch-wise perspectives. Extensive experiments demonstrate that our MSD-Net can achieve remarkable dehazing performance compared with other state-of-the-art methods on several commonly used hazy dataset benchmarks.

Integrated sensing and communication (ISAC) has evolved as a critical paradigm to enhance the dual functions concurrently. However, ISAC may encounter performance limitations, due to undesired channel conditions, small target size, and security threats. In this paper, we investigate intelligent reconfigurable surface (IRS)-aided bi-static ISAC networks, where the IRS is mounted directly on the target surface, and analyze the signal enhancing and suppressing effects of the target-mounted IRS, respectively. First, we maximize the sensing signal-to-noise ratio (SNR) while satisfying the users’ communication requirements by jointly optimizing the transmit beamforming and IRS reflection. To solve this optimization problem, an alternating optimization algorithm is employed to decouple the optimization variables, followed by the application of successive convex approximation and penalty dual decomposition to solve the subproblems. Second, we consider two threatening scenarios where two adversarial base stations (BSs) intend to capture the information reflected by the target. In the first scenario where the adversarial receiving BS attempts to exploit the reflected ISAC signal, we minimize its received power via optimizing the transmit beamforming and the IRS reflection alternately. In the second scenario where the adversarial transmitting BS emits a dedicated signal to detect the target, we focus on optimizing the IRS reflection. Simulation results are presented to show the effectiveness of the proposed schemes.

Point spread function (PSF) characterizes the intensity distribution characteristics of each object point and is widely used in areas such as defocus estimation, non-blind image deblurring, and computational imaging. Estimating spatially varying PSF from a single image is a typical inverse problem, which is constrained by multiple factors such as sensor noise and semantic information interference. In this paper, we propose a polynomial fitting-based method to model spatially varying PSF. With this method, we generate a large-scale, high-quality dataset with pixel-level annotations that can be used for training deep learning networks. To solve the task of estimating defocus maps from a single image, we design a novel high-resolution coefficient regression network to achieve accurate defocus estimation and concurrent estimation of multiple aberrations, respectively. To the best of our knowledge, this work presents the inaugural attempt at spatially varying PSF estimation based on polynomial coefficient regression. Extensive experimental results show that our methodology attains state-of-the-art performance across numerous evaluation metrics, fully verifying its effectiveness and superiority. The dataset and code is available on GitHub: https://github.com/67689E4F/PSFNet.git.

Visible and infrared image registration is a challenging problem in computer vision due to the significant differences in appearance and physical properties between the two modalities. A single feature is not enough to remove nonlinear differences, and the matching method faces a trade-off between the high-resolution feature map and the transformer model. In this paper, we propose a novel method called Adaptive-Neighborhood Contextual Multi-features (ANCM-Net) for visible/infrared image registration. Our method addresses the limitations of existing approaches by incorporating depth features and cross-modal similar contour features to form contextual feature representations. Additionally, we propose a region-spanning adaptive cross-attention module to handle low spatial resolution and redundancy in attention computation. This module enables attentional encoding of limited information in the attention location and cross-modal adaptive region through attention region adjustment. In the matching task, we compute an adaptive attention region for each pixel point in the cross-modal image and encode and match the depth features and edge features together. As a result, ANCM-Net not only preserves the long-range dependency of the image feature structure but also achieves fine-grained attention between highly correlated pixels. By extracting cross-modal consistent contextual features to compensate for modality-specific information, our approach improves the cross-modal matching performance. Extensive experiments on real-world captured thermal infrared and visible datasets demonstrate that ANCM-Net outperforms existing image matching methods.