Non-AI Multipass Edge and Body Pixel Sharpening Pipeline for High-Resolution Lunar Astrophotography Enhancement
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.