Adaptive Optics Image Restoration Based on Frame Selection and Multi-frame Blind Deconvolution

Tian, Yu; Rao, Changhui; Wei, Kai

doi:10.1016/j.chinastron.2009.03.004

Cited by 6 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each frame was processed with the flat and dark correction first, then a Wiener filter was used to deconvolute the frames with the point spread function (PSF) of the NBI system, which was measured by a point source. image-restoration process based on frame selection and multiframes blind deconvolution (Yu, Chang-hui, and Kai, 2009) was applied to the frames. One frame of the object imaged at each wavelength was obtained after the processing.…”

Section: Data Processing and First Resultsmentioning

confidence: 99%

Fabry–Pérot Interferometer-Based Tunable Narrow-Band Imager for Solar Chromospheric Observation: First Results

Yang

Rao

et al. 2022

Sol Phys

View full text Add to dashboard Cite

Recently, we have developed a tunable narrow-band imager (NBI) with high spectral resolution based on a Fabry–Pérot (FP) interferometer to observe the solar chromosphere. The NBI system is designed for imaging the Sun at the H$\alpha $ α wavelength (6563 Å). The FP interferometer has a bandpass (FWHM) of 0.09 Å and a free spectral range (FSR) of 3.85 Å at $\lambda = 628$ λ = 628 nm, working in combination with a prefilter with a bandpass of 1.6 Å and a center wavelength at 656.3 nm to achieve narrow-band imaging. We have successfully tested the NBI system on the 65-cm Educational Adaptive-optics Solar Telescope (EAST) in the Shanghai Astronomy Museum, and observed the sunspots in active region (AR) 12880 of the Sun on October 2 and 3, 2021. In this paper, we briefly present a description of the instrument, experimental observations, data processing, and the first observational results.

show abstract

Section: Data Processing and First Resultsmentioning

confidence: 99%

Fabry–Pérot Interferometer-Based Tunable Narrow-Band Imager for Solar Chromospheric Observation: First Results

Yang

Rao

et al. 2022

Sol Phys

View full text Add to dashboard Cite

show abstract

“…. , g M } are obtained with frame selection technique [17], then the initial object image iŝ Step 4: If j > MaxIteration, then output object estimate imagef and PSF estimationĥ, and end the algorithm; otherwise go to Step 3. In our method, parameter settings is based on the irace package [21].…”

Section: Algorithm 1 Steps For Our Proposed Algorithmmentioning

confidence: 99%

“…A single-channel blind deconvolution method [16] is proposed by Ayers et al, which is more complicated than the classical deconvolution problem. In order to handle the ill-posed nature of blind deconvolution to a great extent of considering multiple images, Tian et al presented another multiframe restoration algorithm based on the frame selection techiques [17]. Approaches based on improved expectation maximization for AO image restoration was proposed in [18].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Optics Image Restoration via Regularization Priors With Gaussian Statistics

Qiu

Zhang

2020

IEEE Access

View full text Add to dashboard Cite

In order to compensate for any failure on the use of point spread function (blur kernel) estimation and image estimation priors, we propose a novel regularization priors scheme with adapting the parameter for image restoration involving adaptive optics (AO) images. Our scheme uses a maximum a posteriori estimation with Gaussian statistics on the image and point spread function (blur kernel). An efficient regularization prior method associated with alternating minimization method is described to obtain the optimal solution recursively. Our method is applied to synthetic and real adaptive optics images. After applying our restoration method, satisfying results are obtained. Experimental results demonstrate that our proposed model and method performs better for restoring images in terms of both subjective results and objective assessments than the current state-of-the-art restoring methods. In addition, our proposed method can be a new way to promote their performances for AO image restoration.

show abstract

“…Because observations are generally continuous within the generated time of many short exposure AO images, multiframe blind deconvolution algorithms have received more and more attention, which recovers fixed latent objects and corresponding PSFs from multiple observations simultaneously. Löfdahl (2002) proposed multiframe blind deconvolution with linear equality constraints and Yu et al (2009) proposed multiframe blind deconvolution based on frame selection.…”

Section: Introductionmentioning

confidence: 99%

Blind restoration of solar images via the Channel Sharing Spatio-temporal Network

Wang

Chen

et al. 2021

A&A

View full text Add to dashboard Cite

Context. Due to the presence of atmospheric turbulence, the quality of solar images tends to be significantly degraded when observed by ground-based telescopes. The adaptive optics (AO) system can achieve partial correction but stops short of reaching the diffraction limit. In order to further improve the imaging quality, post-processing for AO closed-loop images is still necessary. Methods based on deep learning (DL) have been proposed for AO image reconstruction, but the most of them are based on the assumption that the point spread function (PSF) is spatially invariant. Aims. Our goal is to construct clear solar images by using a sophisticated spatially variant end-to-end blind restoration network. Methods. The proposed channel sharing spatio-temporal network (CSSTN) consists of three sub-networks: a feature extraction network (FEN), channel sharing spatio-temporal filter adaptive network (CSSTFAN), and a reconstruction network (RN). First, CSST-FAN generates two filters adaptively according to features generated from three inputs. Then these filters are delivered to the proposed channel sharing filter adaptive convolutional (CSFAC) layer in CSSTFAN to convolve with the previous or current step features. Finally, the convolved features are concatenated as input of RN to restore a clear image. Ultimately, CSSTN and the other three supervised DL methods are trained on the binding real 705nm photospheric and 656nm chromospheric AO correction images as well as the corresponding speckle reconstructed images. Results. The results of CSSTN, the three DL methods, and one classic blind deconvolution method evaluated on four test sets are shown. The imaging condition of the first photospheric and second chromospheric set is the same as training set, except for the different time given in the same hour. The imaging condition of the third chromospheric and fourth photospheric set is the same as the first and second, except for the Sun region and time. Our method restores clearer images and performs best in both the peak signal-to-noise ratio (PSNR) and contrast among these methods.

show abstract

Adaptive Optics Image Restoration Based on Frame Selection and Multi-frame Blind Deconvolution

Cited by 6 publications

References 6 publications

Fabry–Pérot Interferometer-Based Tunable Narrow-Band Imager for Solar Chromospheric Observation: First Results

Fabry–Pérot Interferometer-Based Tunable Narrow-Band Imager for Solar Chromospheric Observation: First Results

Adaptive Optics Image Restoration via Regularization Priors With Gaussian Statistics

Blind restoration of solar images via the Channel Sharing Spatio-temporal Network

Contact Info

Product

Resources

About