Haze or cloud always shrouds satellite images, obscuring valuable geographic information for military surveillance, natural calamity surveillance and mineral resource exploration. Satellite image dehazing (SID) provides the possibility for better applications of satellite images. Most of the existing dehazing methods are tailored for natural images and are not very effective for satellite images with non-homogeneous haze since the semantic structure information and inconsistent attenuation are not fully considered. To tackle this problem, this study proposes a hierarchical semantic-guided contextual structure-aware network (SCSNet) for spectral satellite image dehazing. Specifically, a hybrid CNN–Transformer architecture integrated with a hierarchical semantic guidance (HSG) module is presented to learn semantic structure information by synergetically complementing local representation from non-local features. Furthermore, a cross-layer fusion (CLF) module is specially designed to replace the traditional skip connection during the feature decoding stage so as to reinforce the attention to the spatial regions and feature channels with more serious attenuation. The results on the SateHaze1k, RS-Haze, and RSID datasets demonstrated that the proposed SCSNet can achieve effective dehazing and outperforms existing state-of-the-art methods.

Infrared small target detection is a critical task in remote sensing applications, such as aerial reconnaissance, maritime surveillance, and early-warning systems. However, due to the inherent characteristics of remote sensing imagery, such as complex backgrounds, low contrast, and limited spatial resolution-detecting small-scale, dim infrared targets remains highly challenging. To address these issues, we propose MFAFNet, a novel Multi-Feature Attention Fusion Network tailored for infrared remote sensing scenarios. The network comprises three key modules: a Feature Interactive Fusion Module (FIFM), a Patch Attention Block (PAB), and an Asymmetric Contextual Fusion Module (ACFM). FIFM enhances target saliency by integrating the original infrared image with two locally enhanced feature maps capturing different receptive field scales. PAB exploits global contextual relationships by computing inter-pixel correlations across multi-scale patches, thus improving detection robustness in cluttered remote scenes. ACFM further refines feature representation by combining shallow spatial details with deep semantic cues, alleviating semantic gaps across feature hierarchies. Experimental results on two public remote sensing datasets, SIRST-Aug and IRSTD-1k, demonstrate that MFAFNet achieves excellent performance, with mean IoU values of 0.7465 and 0.6701, respectively, confirming its effectiveness and generalizability in infrared remote sensing image analysis.

Haze always shrouds satellite images, obscuring valuable geographic information for military surveillance, natural calamity surveillance and mineral resource exploration. Satellite image dehazing (SID) provides the possibility for better applications of satellite images. Most of the existing dehazing methods are tailored for natural images and are not very effective for satellite images with non-homogeneous haze since the semantic structure information and inconsistent attenuation are not fully considered. To tackle this problem, this study proposes a hierarchical semantic-guided contextual structure-aware network (SCSNet) for satellite image dehazing. Specifically, a hybrid CNN-transformer architecture integrated with a hierarchical semantic guidance (HSG) module is presented to learn semantic structure information by synergetically complementing local representation from non-local features. Furthermore, a cross-layer fusion (CLF) module is specially designed to replace the traditional skip connection during the feature decoding stage, so as to reinforce the attention to the spatial regions and feature channels with more serious attenuation. The results on the SateHaze1k, RS-Haze, and RSID datasets demonstrate that the proposed SCSNet can achieve effective dehazing and outperforms existing state-of-the-art methods.

Short-wave infrared imaging technology has rapidly developed over the past decade. However, image detail and contrast enhancement techniques, which are crucial for infrared imaging systems, are rarely dedicated to SWIR imaging systems. Moreover, the existing IR image enhancement methods generally have difficulty providing real-time performance, which plays a significant role in imaging systems with high data rates. In this paper, we propose a simple and real-time SWIR image enhancement approach based on the difference of Gaussian filter and histogram remapping, and illustrate the implementation of the proposed method on FPGA. The experimental results demonstrated that our method achieves promising detail and contrast enhancement performance with a frame rate of around 50 fps for high-definition images, and that extremely high frame rates can be achieved with pipelined architecture.

The combination of single-pixel imaging and single photon-counting technology enables ultra-high-sensitivity photon-counting imaging. In order to shorten the reconstruction time of single-photon counting, the algorithm of compressed sensing is used to reconstruct the underdetermined image. Compressed sensing theory based on prior constraints provides a solution that can achieve stable and high-quality reconstruction, while the prior information generated by the network may overfit the feature extraction and increase the burden of the system. In this paper, we propose a novel sparse autoencoder network prior for the reconstruction of the single-pixel imaging, and we also propose the idea of multi-channel prior, using the fully connected layer to construct the sparse autoencoder network. Then, take the network training results as prior information and use the numerical gradient descent method to solve underdetermined linear equations. The experimental results indicate that this sparse autoencoder network prior for the single-photon counting compressed images reconstruction has the ability to outperform the traditional one-norm prior, effectively improving the reconstruction quality.