Multiframe Correction Blind Deconvolution for Solar Image Restoration

Abstract

A series of short-exposure images are often used for rich, small-scale structure, high-quality, and high-resolution astronomical observations. Postprocessing of the closed-loop adaptive optics (AO) image using ground-based astronomical telescopes plays an important role in astronomical observations due to it further improving image quality after AO processing. These images show several main characteristics: random spatial variation blur kernel, unclear model after AO correction, unclear physical characteristics of observation objects, etc. Our goal is to propose a multiframe correction blind deconvolution (MFCBD) algorithm to restore AO closed-loop solar images. MFCBD introduces a denoiser and corrector to help estimate the intermediate latent image and proposes using an L q norm of the kernel as the sparse constraint to acquire a compact blur kernel. MFCBD also uses the half-quadratic splitting strategy to optimize the objective function, which makes the algorithm not only simple to solve, but also easy to adapt to different fidelity terms and prior terms. In tests on three data sets observed from the photosphere and chromosphere of the Sun, MFCBD not only restored clearer and more detailed images, but also converged smoothly and monotonically in terms of the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) after a few iterations. Taking the speckle-reconstructed image as a reference, the clear image restored by our method performs best both in PSNR and SSIM compared with the state-of-the-art traditional methods OBD and BATUD.

Cascaded Temporal and Spatial Attention Network for solar adaptive optics image restoration

“…Other sophisticated and more interpretable processes, such as self-attention and diffusion probabilistic models, may be worth exploring further (Deng et al 2021;Song et al 2022). Meanwhile, compared with non-deep learning methods of restoring solar AO images (Wang et al 2022), a systematic theory of deep learning is urgently needed to support its wide application in actual astronomy imaging and observation scenario requiring high reliability and interpretability.…”

Comparative Analysis of Image-shift Measurement Algorithms for Solar Shack–Hartmann Wavefront Sensors

“…The presence of unstable airflow in the Earth's atmosphere leads to image blurring and distortion, ultimately reducing the resolution of the observations. Ground-based telescopes are commonly equipped with adaptive optics (AO) systems (Roddier 1999; Changhui et al 2018;Guo et al 2022;Wang et al 2022) to mitigate the impact of atmospheric turbulence on solar observations. These AO systems have the ability to detect and correct optical distortions induced by atmospheric turbulence in real-time, thereby greatly enhancing the spatial resolution of solar observations (Tyson & Frazier 2022).…”