Low signal-to-noise ratio (SNR) infrared point target detection and tracking is crucial to study regarding infrared remote sensing. In the low-SNR images, the intensive noise will submerge targets. In this letter, a saliency-guided double-stage particle filter (SGDS-PF) formed by the searching particle filter (PF) and tracking PF is proposed to detect and track targets. Before the searching PF, to suppress noise and enhance targets, the single-frame and multi-frame target accumulation methods are introduced. Besides, the likelihood estimation filter and image block segmentation are proposed to extract the likelihood saliency and obtain proper proposal density. Guided by this proposal density, the searching PF detects potential targets efficiently. Then, with the result of the searching PF, the tracking PF is adopted to track and confirm the potential targets. Finally, the path of the real targets will be output. Compared with the existing methods, the SGDS-PF optimizes the proposal density for low-SNR images. Using a few accurate particles, the searching PF detects potential targets quickly and accurately. In addition, initialized by the searching PF, the tracking PF can keep tracking targets using very few particles even under intensive noise. Furthermore, the parameters have been selected appropriately through experiments. Extensive experimental results show that the SGDS-PF has an outstanding performance in tracking precision, tracking reliability, and time consumption. The SGDS-PF outperforms the other advanced methods.

In a space-based infrared system, the enhancement of targets plays a crucial role in improving target detection capabilities. However, the existing methods of target enhancement face challenges when dealing with multi-target scenarios and a low signal-to-noise ratio (SNR). Furthermore, false enhancement poses a serious problem that affects the overall performance. To address these issues, a new enhancement method for a dim moving multi-target with strong robustness for false enhancement is proposed in this paper. Firstly, multi-target localization is applied by spatial–temporal filtering and connected component detection. Then, the motion vectors of each target are obtained using optical flow detection. Finally, the consecutive images are convoluted in 3D based on the convolution kernel guided by the motion vectors of the target. This process allows for the accumulation of the target energy. The experimental results demonstrate that this algorithm can adaptively enhance a multi-target and notably improve the SNR under low SNR conditions. Moreover, it exhibits outstanding performance compared to other algorithms and possesses strong robustness against false enhancement.

An infrared camera is affected by the photon effect, temperature changes, time drift and other factors when operating in orbit, which makes the ground non-uniformity correction coefficient invalid, resulting in non-uniformity stripes in the infrared images and restricting their practicality in further analysis and applications. Existing destriping methods often suffer from loss of image details and artifact generation. To solve this problem, we proposed a weighted guided filtering-based multi-domain fusion destriping approach that leverages the structural, directional, and spectral characteristics of stripe noise. Firstly, we addressed the issue of artifacts caused by Fourier domain filtering through an adaptive filtering approach that employs a variable threshold to minimize filtering induced artifacts and obtain clearer guided images. Furthermore, capitalizing on the directional properties of wavelet decomposition effectively separates image information from stripe information. To integrate the advantages of both approaches, we employed a weighted guided filter to seamlessly fuse the guided image with the wavelet decomposition image. In terms of quantitative metrics, the proposed method generally beats the other five comparative methods, with significant improvements in image PSNR, SSIM, NIQE, and MRD, particularly for complex images where the enhancements were more pronounced. These experimental results collectively demonstrate the significant progress achieved by the proposed method in effectively reducing stripe noise, better preserving the original structural details of the image, and suppressing the occurrence of artifacts.