A Novel Hybrid Algorithm for Lucky Imaging

TL;DR

This paper introduces a hybrid lucky imaging algorithm combining spatial and frequency domain techniques, improving image resolution and memory efficiency for astronomical imaging.

Contribution

It presents a novel hybrid algorithm that integrates classic lucky imaging methods, enhancing resolution and reducing memory usage, suitable for standard desktop computers.

Findings

Superior high-resolution images compared to classic algorithms

Memory-efficient scheme enables processing larger datasets

Effective binary star detection under various atmospheric conditions

Abstract

Lucky imaging is a high-resolution astronomical image recovery technique with two classic implementation algorithms, i.e. image selecting, shifting and adding in image space and data selecting and image synthesizing in Fourier space. This paper proposes a novel lucky imaging algorithm where with space-domain and frequency-domain selection rates as a link, the two classic algorithms are combined successfully, making each algorithm a proper subset of the novel hybrid algorithm. Experimental results show that with the same experiment dataset and platform, the high-resolution image obtained by the proposed algorithm is superior to that obtained by the two classic algorithms. This paper also proposes a new lucky image selection and storage scheme, which can greatly save computer memory and enable lucky imaging algorithm to be implemented in a common desktop or laptop with small memory and to process astronomical images with more frames and larger size. Besides, through simulation analysis, this paper discusses the binary star detection limits of the novel lucky imaging algorithm and traditional ones under different atmospheric conditions.