Learning based approaches have witnessed great successes in blind single image super-resolution (SISR) tasks, however, handcrafted kernel priors and learning based kernel priors are typically required. In this paper, we propose a meta-learning and Markov Chain Monte Carlo (MCMC) based SISR approach to learn kernel priors from organized randomness. In concrete, a lightweight network is adopted as kernel generator, and is optimized via learning from the MCMC simulation on random Gaussian distributions. This procedure provides an approximation for the rational blur kernel, and introduces a network-level Langevin dynamics into SISR optimization processes, which contributes to preventing bad local optimal solutions for kernel estimation. Meanwhile, a meta-learning based alternating optimization procedure is proposed to optimize the kernel generator and image restorer, respectively. In contrast to the conventional alternating minimization strategy, a meta-learning based framework is applied to learn an adaptive optimization strategy, which is less-greedy and results in better convergence performance. These two procedures are iteratively processed in a plug-and-play fashion, for the first time, realizing a learning-based but plug-and-play blind SISR solution in unsupervised inference. Extensive simulations demonstrate the superior performance and generalization ability of the proposed approach when compared with the Start-of-the-Art solutions on synthesis and real-world datasets.

Hyperspectral image super-resolution (HSI-SR) is fundamentally more difficult than RGB image SR, since its ultrahigh spectral dimensionality. Existing supervised methods rely on labeled training data to obtain data prior, which incurs prohibitive collection costs and limits generalization. Unsupervised methods individually preset the band and kernel with handcrafted priors, whereas this decoupling modeling artificially creates a complexity-performance trade-off in the selected band number. To address these issues, we propose BKX-HMM, a unified statistical framework for blind HSI-SR, which uniformly models the band selection, kernel estimation, and HSI restoration through the state transition of a hidden Markov model (HMM). BKX-HMM redefines the trade-off as a distributional fitting problem: each Markov transition progressively learns optimal parameters of a full-band distribution via limited spectral observations. Based on BKX-HMM, we propose BKSR, the first unsupervised blind HSI-SR method, which consists of three synergistic modules: Gibbs sampling-based band selection (GBS), test-time-training kernel estimation (TKE), and robust HSI restoration (RHR). These modules form a closed-loop optimization cycle: i) In GBS, the dynamic ergodicity of Gibbs sampling provides a global spectral view for kernel estimation and HSI restoration while maintaining local spectral computations; ii) In TKE, the GBS-sampled bands guide the kernel estimator update, achieving a learnable sampling-based mechanism, which refines kernel estimation to regularize RHR's diffusion trajectory; iii) In RHR, a spectral hyper-Laplacian prior is integrated into the reverse process of an off-the-shelf diffusion model, which achieves non-i.i.d. noise robust HSI restoration, feedback reweights band and kernel importance for subsequent GBS and TKE iterations. Extensive experiments on both synthetic and real HSI datasets demonstrate our BKSR's superiority over baseline methods across diverse scenarios (e.g., unknown Gaussian/motion kernel, non-i.i.d. noise) while maintaining comparable computational costs to the classic band selection methods. The code is available at https://github.com/XYLGroup/BKSR https://github.com/XYLGroup/BKSR.

Attitude estimation of space targets can reveal crucial details about payload orientation, movement intentions, and observation area, all of which are vital in space situational awareness. Till now, inverse synthetic aperture radar (ISAR) has become a mainstream sensor for space target observation, providing rich information for space target attitude estimation. Based on projection matrix and linear structure extracted from ISAR images, a space target attitude estimation method is proposed in this work. The main contribution falls on two parts. On the one hand, linear structure is derived based on the peak accumulation values of original ISAR images rather than binary images. On the other hand, the space target attitude information is effectively estimated based on the projection matrix theory and linear structure extraction results. Experimental studies with measured and simulated data demonstrate the effectiveness of the proposed method.

Physical parameter estimation of satellite is crucial in space situation awareness as it reflects valuable information. Polarimetric inverse synthetic aperture radar (Pol-ISAR) is a powerful sensor for space surveillance, providing rich information for satellite physical parameter estimation. Parabolic antennas, which are widely loaded in remote sensing and communication satellites, have received great attention recently. Dedicated to the space situation awareness issue using Pol-ISAR, a physical parameter joint estimation method of satellite parabolic antenna with key frame Pol-ISAR images is developed in this work. The core idea is to utilize the mapping relationship between parabolic antenna in 3-D space and its projection ellipse in 2-D ISAR image. Under special observation geometry, the closed-form expressions of parabolic antenna physical parameters are deduced for the first time, providing an efficient way for parameter estimation. Moreover, the abundant information within Pol-ISAR images is mined and utilized. Various polarimetric features are adopted for ellipse extraction and the subsequent physical parameter estimation. Compared with single-polarization channel data, the superiorities of polarimetric feature are validated using electromagnetic simulation data.