In a real-world infrared (IR) imaging system, effectively learning a consistent stripe noise removal model is essential. Most existing destriping methods cannot precisely reconstruct images due to cross-level semantic gaps and insufficient characterization of the global column features. To tackle this problem, we propose a novel IR image destriping method, called asymmetric sampling correction network (ASCNet), that can effectively capture global column relationships and embed them into a U-shaped framework, providing comprehensive discriminative representation and seamless semantic connectivity. Our ASCNet consists of three core elements: residual Haar discrete wavelet transform (RHDWT), pixel shuffle (PS), and column nonuniformity correction module (CNCM). Specifically, RHDWT is a novel downsampler that employs double-branch modeling to effectively integrate stripe-directional prior knowledge and data-driven semantic interaction to enrich the feature representation. Observing the semantic patterns crosstalk of stripe noise, PS is introduced as an upsampler to prevent excessive a priori decoding and performing semantic-bias-free image reconstruction. After each sampling, CNCM captures the column relationships in long-range dependencies. By incorporating column, spatial, and self-dependence information, CNCM well establishes a global context to distinguish stripes from the scene’s vertical structures. Extensive experiments on synthetic data, real data, and IR small target detection (IRSTD) tasks demonstrate that the proposed method outperforms state-of-the-art single-image destriping methods both visually and quantitatively. The code is available athttps://github.com/xdFai/ASCNet.

Hyperspectral images (HSIs) often suffer from degradation caused by mixed noise, leading to a decline in the performance of subsequent advanced applications. To eliminate noise and improve image quality, transformer-based approaches have been successfully employed. Nevertheless, these strategies often involve large-scale modeling and tedious layer normalization, which causes inefficiencies during the denoising process. Additionally, the neglect of local spectral correlations in HSIs damages the physical properties in recovery, resulting in poor generalization and inefficient denoising performance. To address these problems, we propose an efficient spatial–spectral oriented triple attention network, dubbed S2OTAN, for HSI denoising. Specifically, to fully exploit the physical properties of HSIs, we impose spatial and spectral multiscale hybrid attention in the single-transformer block side-by-side to fuse spatial–spectral information in a parallel manner. For spatial feature extraction, we introduce hybrid spatial attention by constructing attention maps for pixels within and across windows to exploit the local and global similarity in spatial and improve computational efficiency. For spectral feature exploration, we utilize spectral partitioning operations to enhance the adjacent spectral dependences of HSIs and capture contextual information related to correlations. Consequently, our method exhibits a robust feature representation capability for removing mixed noise in HSIs. Extensive experiments on synthetic and real-world noisy scenarios demonstrate that the proposed approach outperforms other state-of-the-art approaches among quantitative metrics and visual effects. For the sake of reproducibility, the code is available at:https://github.com/Zilong-Xiao/S2OTAN.

Improving hyperspectral image (HSI) quality is crucial in subsequent applications. Current transformer-based methods effectively remove mixed noise from the original HSIs. However, there is limited research on targeted modeling of the spatial locality and edge properties of HSI. In addition, the original transformer computes global query-key pairs indifferently, resulting in equal weights for dissimilarity features, noise, and essential information, thus interfering with the restoration of clean images. To address these issues, this study proposes an effective HSI denoising network called multiscale-sparse spatial-spectral transformer (MS3T) to achieve end-to-end mixed noise removal. Specifically, we reconstruct the attention module in the original transformer by adopting a dual-stream approach to selectively explore the 3-D information of HSI from both spatial and spectral perspectives. In the spatial domain, we construct a multiscale context-capturing module based on the local and non-local similarity properties of HSI to establish remote connections from local to global. In the spectral domain, we develop a top-k selection operator to calculate the similarity scores of query-key pairs and select important semantic information for efficient feature aggregation. Both of the above modules alleviate the computational complexity issue of the original transformer to different extents, and improve the mixed noise removal performance of the whole network. To validate the effectiveness and efficiency of MS3T, we conduct synthetic and real experiments on multiple datasets, and the final results demonstrate that our method outperforms the state-of-the-art methods in both metric evaluation and visual effects; the reproducible code is available athttps://github.com/Zilong-Xiao/MS3T.

In order to effectively utilize spectral and object spatial information to improve tracking performance, we design an Hyperspectral Video (HSV) tracker, namely DSP-Net, to integrate the various prior information. The gradient difference between spectral vectors is explored to develop a clustering technique. The approach generates a binary mask containing target spectral information and appearance clues. A feature cache is introduced to store historical information. Additionally, the channel shift operation is used on the timing to capture the trajectory clues of the target. With the help of the non-local mechanism, the trajectory clues, appearance clues and spectral information of the target are finally integrated, using the designed spectral-spatial joint perception module to enhance the expression of the target. Experimental results show that DSP-Net outperforms state-of-the-art HSV trackers on existing dataset.

Recently, autoencoder-based hyperspectral anomaly detection methods have demonstrated excellent performance on hyperspectral images (HSIs). The autoencoder (AE) can simultaneously reconstruct both the anomaly targets and background, but the lack of prior information limits ability to detect anomalies. This study proposes a novel hyperspectral anomaly detection method based on a guided AE to reduce the feature representation for anomaly targets. First, a multi-layer AE network with skip connections is proposed to fully extract the abundant latent features from HSIs and enhance the expressive ability of the network. The reconstructed HSI can be obtained by the proposed AE network. Second, to suppress anomaly targets in the obtained reconstructed HSI and better represent background features, a guided module based on a guided image is added to the network to reduce the feature representation of anomaly targets by providing feedback information. Moreover, the guided image is calculated using a proposed spectral similarity method that uses the local spatial features of the HSI. Finally, we use the reconstruction error as a performance metric and compare the results of our proposed method with other state-of-the-art methods on six real-world HSIs. The results demonstrate the effectiveness and superiority of the proposed method.

Due to the limited resolution of hyperspectral sensors, anomalous targets expressed with subpixels are often mixed with nonhomogeneous backgrounds. This fact makes anomalies difficult to distinguish from the surrounding background. From this perspective, a novel hyperspectral anomaly detection (AD) algorithm based on endmember extraction and low-rank representation (LRR) is proposed. For the characteristics of pixels in hyperspectral images (HSIs), the proposed algorithm employs an endmember extraction technology to yield an abundance matrix for AD, thereby gathering more feature information compared with the direct use of a raw image. In addition, a dictionary construction strategy based on Tucker decomposition, and the${k}$-means++ clustering method is proposed to make the dictionary more stable and discriminative. An LRR method based on the dictionary is applied to obtain a sparse residual matrix. Finally, anomalies can be determined by the response of the residual matrix. Experiments on three hyperspectral data sets validate the performance of the proposed algorithm.

Thermal infrared (TIR) imaging has been an indispensable tool in surveillance and remote sensing fields due to the characteristic of this spectrum that enables the sensing system to detect relatively warm targets, especially in low-light conditions. However, the acquired TIR images often suffer from observable stripe noise, which reduces the target detectability to some extent. To remove the noise and keep the image details, this letter proposes a novel method that combines the spectral processing technology with the image-guidance mechanism. Specifically, the frequency band contaminated by stripe noise is corrected with the corresponding Fourier coefficients of a guided image, which can be estimated by existing smoothing methods. Various experiments on the simulated and real TIR images show high performance and efficiency of the proposed method. In addition, in the application of small target detection, it is demonstrated that local contrast between the target and its background is well maintained and the signal-to-clutter ratio is increased when our method is performed.

Due to the low signal to noise ratio and limited spatial resolution, small target detection in an infrared image is a challenging task. Existing methods often have high false alarm rates and low probabilities of detection when infrared small targets submerge in the background clutter. In this paper, the Convolutional Neural Network (CNN) is adapted to extract the hidden features of small targets from infrared imagery with a proposed technique for a large amount of training data generation. The Point Spread Function (PSF) is employed to model the small target data and generate positive samples. The random background image patches are selected as the negative samples. In this way, the detection problem is skillfully converted into a problem of pattern classification using CNN. Extensive synthetic and real small targets were tested to evaluate the performance of this novel small target detection framework. The experimental results indicate that the proposed algorithm is simple and effective with satisfactory detection accuracy.