Infrared dim small target detection is the critical technology in the situational awareness field currently. The detection algorithm of the infrared patch image (IPI) model combined with the total variation term is a recent research hotspot in this field, but there is an obvious staircase effect in target detection, which reduces the detection accuracy to some extent. This paper further investigates the problem of accurate detection of infrared dim small targets and a novel method based on total variation weighted low-rank constraint (TVWLR) is proposed. According to the overlapping edge information of image background structure characteristics, the weights of constraint low-rank items are adaptively determined to effectively suppress the staircase effect and enhance the details. Moreover, an optimization algorithm combined with the augmented Lagrange multiplier method is proposed to solve the established TVWLR model. Finally, the experimental results of multiple sequence images indicate that the proposed algorithm has obvious improvements in detection accuracy, including receiver operating characteristic (ROC) curve, background suppression factor (BSF) and signal-to-clutter ratio gain (SCRG). Furthermore, the proposed method has stronger robustness under complex background conditions such as buildings and trees.

Block‐matching and three‐dimensional filtering (BM3D) is generally considered as a milestone for its outstanding performance in the area of image denoising. However, it still suffers from the loss of image detail due to the utilisation of hard thresholding on transform domain during the phase of the basic estimate. In the frequency domain, a large amount of image detail information is in high frequency, which tends to be mixed with noise. Since its low amplitude is below the threshold, some image detail is filtered out with the noise. To retain more details, this study proposes an improved BM3D. It adopts an adaptable threshold with the core of Gaussian function during hard thresholding, which can filter out more noise while retaining more high‐frequency information. When grouping, the normalised angular distance is taken as a measure of similarity to relieve the interference of noise further and achieve a higher peak signal‐to‐noise ratio (PSNR). The experimental results show that under the background of Gaussian noise with standard deviation of 20–60, the PSNR of denoised images (with a large amount of detail), applied with the authors’ improved algorithm, can be improved by compared with original BM3D.

The purpose of sea image enhancement is to enhance the information of the waves, whose contrast is generally weak. Enhancement effect is often affected by impulse-type noise and non-uniform illumination. In this paper, we propose a variational model for sea image enhancement using a solar halo model and a Retinex model. This paper mainly makes the following three contributions: 1. Establishing a Retinex model with noise suppression ability in sea images; 2. Establishing a solar-scattering halo model through sea image bitplane analysis; 3. Proposing a variational enhancement model combining the Retinex and halo models. The experimental results show that our method has a significant enhancement effect on sea surface images in different illumination environments compared with typical methods.

A novel autofocusing algorithm using the directional wavelet power spectrum is proposed for time delayed and integration charge coupled device (TDI CCD) space cameras, which overcomes the difficulty of focus measure for the real-time change of imaging scenes. Using the multiresolution and band-pass characteristics of wavelet transform to improve the power spectrum based on fast Fourier transform (FFT), the wavelet power spectrum is less sensitive to the variance of scenes. Moreover, the new focus measure can effectively eliminate the impact of image motion mismatching by the directional selection. We test the proposed method's performance on synthetic images as well as a real ground experiment for one TDI CCD prototype camera, and compare it with the focus measure based on the existing FFT spectrum. The simulation results show that the new focus measure can effectively express the defocused states for the real remote sensing images. The error ratio is only 0.112, while the prevalent algorithm based on the FFT spectrum is as high as 0.4. Compared with the FFT-based method, the proposed algorithm performs at a high reliability in the real imaging experiments, where it reduces the instability from 0.600 to 0.161. Two experimental results demonstrate that the proposed algorithm has the characteristics of good monotonicity, high sensitivity, and accuracy. The new algorithm can satisfy the autofocusing requirements for TDI CCD space cameras.

To address the challenge of imaging quality degradation in the highly directional star camera under micro-vibration loads, this study calculated the optical sensitivity equation, mapped the rigid body displacements of optical components to image motions, and proposed a quantitative method for evaluating the dynamic imaging quality. It conducted simulation calculations on the imaging quality degradation of the star camera under micro-vibration. Results show that the imaging quality of the star camera does not degrade. Through a sweep frequency vibration test, the relative errors between the test results and the simulation results of the first-order frequencies in the X/Y/Z directions were 3.02%, 2.04%, and 1.67%, respectively. Through an image motion measurement test, the relative errors between the test results and the simulation results of the image motions in the X/Y directions were 8.12% and 9.95%, respectively. These verified the rationality of the star camera structure and the accuracy of the simulation results. The findings provide guidance for the structural optimization of space optical star cameras and the design of vibration isolation systems.

Hyperspectral imaging is an important technology in remote sensing, which is widely used in many fields such as agriculture and forestry observation, environmental monitoring, deep space exploration, etc. The miniaturization and low cost have become an important development direction for hyperspectral imagers. Simultaneously obtaining images high spatial resolution, high spectral resolution, and high signal-to-noise ratio (SNR) has become an urgent challenge in the development of miniaturized imaging spectrometers. Based on linear variable filter (LVF) spectroscopy technology and small pixel CMOS sensors, combining with time delay and integration (TDI) algorithm in digital domain, we proposed a digital integrated LVF spectral imaging method, then a miniaturized hyperspectral imaging system which weighs only 5 kg was designed. To address the spectral aliasing problem of this new imaging method, we further proposed a calibration based spectral restoration and enhancement algorithm. The experimental results showed that the miniaturized hyperspectral imager can achieve a ground sample distance (GSD) of 10m@500km, an average spectral resolution of 5.6nm, and SNR better than 30dB.