Infrared small target detection (IRSTD) has been a long challenging task in infrared detection technology due to the inherently low contrast and absence of discernible texture in such targets. To address this issue, a new U-shaped segmentation network (CN-UNet) is proposed in this article. CN-UNet first employs the ConvNeXt module as the encoder, which implements a self-attention mechanism through pure convolutional operations, mirroring the Swin Transformer’s functionality while expanding the receptive field interactions of target features. This design not only optimizes computational efficiency but also enables multiscale feature identification across diverse dimensional contexts. Besides, a multiscale local contrast learning module (MSLCLM) is integrated into the encoder-decoder cross layer connections to selectively filter features aligned with human visual priors, thereby directing the network’s attention toward precise target localization. Finally, during the model inference, a slicing-aided hypersegmentation module is introduced to dynamically rescale output images through adaptive slicing operations, which could furtherly improve infrared target detection and segmentation performance. Comprehensive evaluations across three typical benchmarks demonstrate that the proposed CN-UNet is superior to other state-of-the-art IRSTD methods, with better visual quality and index evaluation metrics.

Infrared small target detection (IRSTD) remains a long challenging problem in infrared imaging technology. To enhance detection performance while more effectively exploiting target-specific characteristics, a novel U-shaped segmentation network called knowledge-embedded CSwin-UNet (KECS-Net) is proposed in this letter. KECS-Net first incorporates a CSwin transformer module into the encoder of the UNet backbone, enabling the extraction of multiscale features from infrared targets within an expanded receptive field, while achieving higher computational efficiency compared to the original Swin transformer. Besides, a multiscale local contrast enhancement module (MLCEM) is introduced, which utilizes hand-crafted dilated convolution operators to amplify locally salient target responses and suppress background noise, thereby guiding the model to focus on potential target regions. Finally, a slicing-aided hypersegmentation (SAHS) method is also designed to resize and rescale the output image, increasing the relative size of small targets and improving segmentation accuracy during inference. Extensive experiments on three benchmark datasets demonstrate that the proposed KECS-Net outperforms the state-of-the-art (SOTA) methods in both quantitative metrics and visual quality. Relevant code will be available at https://github.com/Lilingxiao-image/KECS-Net

Infrared imaging systems are widely used across industries. However, their output images often exhibit striped noise due to the nonuniform response of the detection system, which significantly affects image quality and visual fidelity. To address challenges such as incomplete stripe removal, potential loss of image details and textures, and the generation of artificial artifacts during destriping, we propose a novel stripe removal method based on a knowledge-embedded diffusion model (KEDM). This approach effectively integrates the spatial distribution characteristics of stripe noise with an innovative, data-driven diffusion network model, creating a hybrid knowledge and data-driven framework for stripe correction. The core components of KEDM are the latent diffusion model (LDM) architecture and the directional wavelet convolution module (DWCM). Specifically, LDM leverages a pretrained variational autoencoder (VAE) to transform the input image into latent feature space for efficient diffusion propagation, reducing computational complexity while preserving image restoration quality. Meanwhile, DWCM uses wavelet convolution operations to construct prior loss functions for stripe noise, precisely guiding the diffusion reconstruction process to achieve a clean, stripe-free image. Empirical evaluations on several benchmark datasets demonstrate that the proposed KEDM outperforms other state-of-the-art destriping algorithms in terms of visual quality and quantitative metrics, validating its excellent performance.

Detecting and identifying small infrared targets has always been a crucial technology for many applications. To address the low accuracy, high false-alarm rate, and poor environmental adaptability that commonly exist in infrared target detection methods, this paper proposes a composite infrared dim and small target detection model called USES-Net, which combines the target prior knowledge and conventional data-driven deep learning networks to make use of both labeled data and the domain knowledge. Based on the typical encoder–decoder structure, USES-Net firstly introduces the self-attention mechanism of Swin Transformer to replace the universal convolution kernel at the encoder end. This helps to extract potential features related to dim, small targets in a larger receptive field. In addition, USES-Net includes an embedded patch-based contrast learning module (EPCLM) to integrate the spatial distribution of the target as a knowledge prior in the training network model. This guides the training process of the constrained network model with clear physical interpretability. Finally, USES-Net also designs a bottom-up cross-layer feature fusion module (AFM) as the decoder of the network, and a data-slicing-aided enhancement and inference method based on Slicing Aided Hyper Inference (SAHI) is utilized to further improve the model’s detection accuracy. An experimental comparative analysis shows that USES-Net achieves the best results on three typical infrared weak-target datasets: NUAA-SIRST, NUDT-SIRST, and IRSTD-1K. The results of the target segmentation are complete and sufficient, which demonstrates the validity and practicality of the proposed method in comparison to others.

Non-uniformity commonly exists in the infrared focal plane (IRFPA), which behaves as the fixed pattern noise (FPN) and seriously affects the image quality of infrared imaging system. This paper proposed a novel scene-based non-uniformity correction method with a new edge-preserve filter and adaptive learning rate. Firstly, using co-occurrence filter as the desired image estimation, the proposed method removed the FPN while preserving the image details. Then an adaptive learning rate connected with both temporal motion and spatial correlation factor is utilized to decrease the effect of ghosting artifacts. In this way the proposed method overcomes the shortcomings of traditional scene-based non-uniformity. Several real infrared image sequences collected in different conditions are used to verify the performance of proposed method. The experiment results demonstrate that the proposed method has much better visual effect, making a great balance between the non-uniformity correction and details preservation. Compared with other good NUC method, this method also has better performance in the aspects of applicability and robustness, which has great application value.

Aiming at the problems of low contrast, low signal-to-noise ratio and scattered energy in the field of optical detection and imaging, a visual enhancement and multi-scale decomposition method for fusion of visible and infrared images is proposed based on the visual characteristics of different bands’ image. Firstly, the infrared image with less texture information and low contrast is preprocessed to weaken the background noise and improve the visual contrast. On this basis, the multi-scale image is decomposed by detail preserving filter, and the saliency map of each scale image is obtained by saliency extraction method. Then, the multi-source images of each scale are fused with saliency map, and binary filtering fusion rules are adopted for the regions with salient details while weighted fusion rules are adopted for other regions. Finally, the images of all scale are reconstructed to get better fusion results. The experimental results show that this method can significantly improve the visual contrast of the fused object, and the objective evaluation indexes are superior to other comparison methods.