2026· Asian journal of applied science and technology· 0 citations
TL;DR
An enhancement pipeline that operates entirely within classical signal processing is proposed, providing a transparent alternative to black-box machine learning methods while remaining practical on standard personal computers.
Abstract
Processing the lunar astrophotography imagery is challenging since atmospheric turbulence and low-light conditions introduce blur and noise that
obscure small-scale lunar features. The present work proposes an enhancement pipeline that operates entirely within classical signal processing,
providing a transparent alternative to black-box machine learning methods while remaining practical on standard personal computers. The pipeline
integrates four key stages: lucky-imaging frame selection and stacking, wavelet-domain denoising, adaptive local contrast enhancement, and
dual-stage edge sharpening. The pipeline is evaluated on 30 lunar datasets spanning multiple phases and multiple observable conditions. Quantitative
results show Peak Signal-to-Noise Ratio (PSNR) improvements of approximately 6.2–12.4 dB and an increase in Structural Similarity Index
Measure (SSIM) from 0.56 to 0.90, indicating better structural fidelity relative to stacked baselines. Stacking of 16 carefully selected frames yields
effective SNR gains of up to about 3.2 times, while modulation transfer function (MTF) analysis at limb and crater-edge boundaries reveals sharpness
improvements in the order of 28–35%. The workflow requires no specialized accelerators, with typical resource usage of roughly 4.2 MB memory
and 2.3 seconds per megapixel on conventional CPUs. This is demonstrating that classical, interpretable techniques remain highly competitive for
scientific and educational lunar image enhancement.
Infrared imaging has unique advantages in remote sensing observation and non-contact measurement, but its inherent low contrast and blurred structural details limit the reliability of scene interpretation by human observers. Unlike deep learning-based approaches that rely on data-driven training and substantial computational resources, we propose a Hybrid Prior Enhanced Decomposition (HPED) model, a training-free, model-driven framework that incorporates structural and luminance priors into a multi-stage enhancement pipeline. An l1–l0-regularized decomposition separates the input into a base layer that preserves global structures and salient edges and a detail layer in which low-amplitude fluctuations and noise are suppressed. A prior-preserving bi-gamma correction method enhances base-layer contrast through prior-guided histogram segmentation and adaptive gray-level redistribution. An improved grayscale mapping strategy further enhances global contrast while maintaining interframe consistency. Experiments on real SWIR, MWIR, and LWIR images show that HPED ranks first among evaluated traditional and deep learning-based methods on key perceptual quality metrics (SSIM, VIF, LIF), while achieving over 25 fps on a CPU-only platform, sufficient for smooth real-time visual display. Task-oriented evaluation further shows that the HPED improves CNR and SCR by 174.7 ± 11.4% and 298.5 ± 52.1% on average over the raw input, outperforming all competing methods and suggesting potential applicability in downstream machine perception tasks such as detection and tracking.
Jie Li, Cheng Wang, Xiangyu Li et al.· Remote Sensing· 0 citations
GPE-YOLO is proposed, a robust detection framework built upon the YOLOv11 architecture that explicitly integrates multiscale edge priors to enhance feature resilience and validate the potential of GPE-YOLO for reliable deployment in real-world adverse weather scenarios.
Xiaojie Chen, Yifei Zhou, Yiming Zhou et al.· International Conference on...· 0 citations
Synthetic aperture radar (SAR) image enhancement faces inherent difficulties in simultaneous speckle suppression and structural detail preservation, severely limiting the performance of conventional methods in high-precision remote sensing tasks. This paper proposes a novel two-stage cascaded deep learning framework for high-quality SAR image reconstruction, which sequentially integrates a window-based Transformer denoising subnet and a diffusion-driven super-resolution subnet via a learnable projection matrix. To address the coupled degradation of speckle noise, imaging blurring, and structural loss, four dedicated designs are adopted, including a projection-coupled serial architecture for joint optimization and lightweight parameter deployment (24.3% parameter reduction), a Frequency Enhancement Module for high-frequency detail recovery, an Adaptive Noise Scheduler for robust diffusion adjustment under complex textures and low-contrast conditions, and Meta Residual Connections for stable deep feature propagation. Quantitative experiments on the FAIR-CSAR-V1.0 ×4 super-resolution benchmark demonstrate that the proposed method achieves state-of-the-art performance with 19.26 dB PSNR, 0.3502 SSIM, 2.23 ENL, and 2.26 RadRes. Our method outperforms the baseline model by 5.71% in PSNR and 30.4% in SSIM, and surpasses existing SOTA methods by 31.9%, 108.2%, 105.6%, and 27.3% in four metrics, respectively, with prominent gains mainly obtained in challenging noisy and low-contrast SAR regions. Ablation studies validate the efficacy of each component. The proposed framework provides an effective lightweight solution for high-precision SAR reconstruction applicable to military reconnaissance, geological exploration, and disaster monitoring.
Wenqiang Cao, Yarong Chen, Zhu Rui et al.· Measurement science and tech...· 0 citations
Comprehensive evaluations on multiple datasets and SR scales indicate that the SVRCL-SR achieves superior performance in artifact suppression and high-frequency detail restoration, along with strong robustness.
Qian Tong, Chaoliang He, Chuandong Tan et al.· Measurement science and tech...· 0 citations
Accurate satellite tracking requires up-to-date Two-Line Elements (TLEs), as outdated data can lead to significant positioning errors. While ground-based optical telescopes are highly accessible, generating TLEs from their data is complicated by the fundamental lack of direct range measurements. This paper presents an automated end-to-end pipeline designed to overcome this limitation by proposing a robust method to estimate the range from prior TLE. The pipeline can then generate the updated TLEs by calculating new satellite state vectors using the estimated range. The pipeline consists of star and satellite detection, astrometric calibration, and orbit determination. For star and satellite detection, the key component of the pipeline is a robust deep learning-based detection model. To achieve this, we benchmarked models such as Deformable DETR, RF-DETR, and YOLOv12 against traditional image processing methods with 3105 images in FITS (Flexible Image Transport System) format. RF-DETR yields the highest F1 score (0.93) and precision (0.97) at an 8-pixel threshold. The detected star coordinates resulting from using RF-DETR, the best-performing model, were fed into Astrometry.net for precise astrometric calibration to determine the satellite celestial coordinates. The range required for orbit determination was estimated by extracting the prior TLE and propagating it to the observation epoch via SGP4. The satellite state vectors were then calculated using TLE-constrained orbit determination approach using the estimated range, followed by an inverse SGP4 optimization to recover the mean orbital elements. The generated TLEs were validated against public TLEs from Space-Track.org. To evaluate this pipeline, updated TLEs were generated specifically for medium Earth orbit (MEO) and geostationary Earth orbit (GEO) targets. The results demonstrate that the proposed method yields high accuracy for GEO satellites, achieving a mean motion difference of 0.0030 rev/day and a 24-hour ground-track position error of 0.83 degrees. In comparison, MEO satellites achieve a mean motion difference of 0.0126 rev/day and an error of 5.27 degrees. These results suggest that the proposed pipeline provides a robust foundation for automated orbit determination with clear potential for further refinement.
Kamin Kanchanapradit, K. Noysena, R. Lipikorn· IEEE Access· 0 citations
Computational imaging shifts part of the aberration correction from optical hardware to algorithms, offering a viable path toward compact, simplified systems. However, overexposed regions—often caused by phenomena such as water-body reflections—can readily induce severe ringing artifacts in reconstructed images. To address this problem, we propose a Ringing-perceptive Cooperative Reconstruction Network (RPCR-Net). This network integrates a learned Wiener filter and a field-of-view shared kernel prediction network (FOV-KPN) for feature extraction and innovatively incorporates a combined regularization mechanism that leverages a Local Maximum Gradient Prior and a multi-scale ringing measurement model within its loss function to suppress artifacts while preserving details. Validated on a constructed overexposed image dataset, RPCR-Net improves the Peak Signal-to-Noise Ratio (PSNR) from 29.08 dB to 37.06 dB and the Structural Similarity Index Measure (SSIM) from 0.8795 to 0.9549. Experiments on real-world scenes further confirm its capability to suppress ringing artifacts while maintaining visual quality. The proposed method can generate high-quality images such as image reconstruction and robustness improvement in optical systems.
Dinghao Yang, Y. Xing, Hongmei Li et al.· Journal of Imaging· 0 citations