Multi-frame blind deconvolution using sparse priors

In this paper, we propose a method for multi-frame blind deconvolution. Two sparse priors, i.e., the natural image gradient prior and an l₁-norm based prior are used to regularize the latent image and point spread functions (PSFs) respectively. An alternating minimization approach is adopted to solve the resulted optimization problem. We use both gray scale blurred frames from a data set and some colored ones which are captured by a digital camera to verify the robustness of our approach. Experimental results show that the proposed method can accurately reconstruct PSFs with complex structures and the restored images are of high quality.     

Iterative blind deconvolution of adaptive optics images

Adaptive optics (AO) technique has been extensively used for large ground-based optical telescopes to overcome the effect of atmospheric turbulence. But the correction is often partial. An iterative blind deconvolution (IBD) algorithm based on maximum-likelihood (ML) method is proposed to restore the details of the object image corrected by AO. IBD algorithm and the procedure are briefly introduced and the experiment results are presented. The results show that IBD algorithm is efficient for the restoration of some useful high-frequency of the image.     

Iterative blind deconvolution of adaptive optics images

Adaptive optics (AO) technique has been extensively used for large ground-based optical telescopes to overcome the effect of atmospheric turbulence. But the correction is often partial. An iterative blind deconvolution (IBD) algorithm based on maximum-likelihood (ML) method is proposed to restore the details of the object image corrected by AO. IBD algorithm and the procedure are briefly introduced and the experiment results are presented. The results show that IBD algorithm is efficient for the restoration of some useful high-frequency of the image.     

天文图像多帧盲反卷积收敛性的增强方法

天文图像多帧盲反卷积的收敛性受到初始目标、约束条件和光子噪声等因素的影响.提出了用实际光学成像系统参数确定频率带宽有限约束的方法.用Knox-Thompson 方法重构初始目标相位形成盲反卷积算法的初始目标函数.研究了一种新颖的有效减小光子噪声、边缘效应和振铃现象的方法.根据最大似然估计理论，用期望最大化的优化方法建立了改进的严格约束多帧盲反卷积算法.模拟图像和实际天文图像的复原结果表明，所建立的多帧盲反卷积，可以有效克服大气湍流和减小光子噪声，改善天文观察图像的分辨率，并部分消除光学系统衍射效应对恢复图像的影响. 关键词： 大气光学 天文观测 图像处理和恢复     

Restoration of solar and star images with phase diversity-based blind deconvolution

The images recorded by a ground-based telescope are often degraded by atmospheric turbulence and the aberration of the optical system. Phase diversity-based blind deconvolution is an effective post-processing method that can be used to overcome the turbulence-induced degradation. The method uses an ensemble of short-exposure images obtained imultaneously from multiple cameras to jointly estimate the object and the wavefront distribution on pupil. Based on signal estimation theory and optimization theory, we derive the cost function and solve the large-scale optimization problem using a limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method. We apply the method to the urbulence degraded images generated with computer, the solar images acquired with the swedish vacuum solar telescope (SVST, 0.475m) in La Paima and the star images collected with 1.2-m telescope in Yunnan Observatory. In order to avoid edge effect in the restoration of the solar images, a modified Hanning apodized window is adopted.The star image till can be estored when the defocus distance is measured inaccurately. The restored results demonstrate that the method is efficient for removing the effect of turbulence and reconstructing the point-like or extended objects.     

Multiframe blind deconvolution applied to diverse shift-and-add images of an astronomical object

In astronomical speckle imaging, deconvolving a shift-and-add (SAA) image has an advantage over deconvolving noisy specklegrams, because an SAA image is an integration of many specklegrams and has a relatively enhanced signal-tonoise ratio. In this paper, to reinforce the deconvolution of a single SAA image, we propose a multiframe deconvolution applied to multiple SAA images that are obtained by diversely recombining the same set of specklegrams to have different point spread functions. We have found that such diverse SAA images can be easily produced by permuting specklegrams to be processed by SAA. The results of experiments using simulated and observational data have shown a robustness of our present approach: in the previous approach of deconvolving a single SAA frame, the resulting object estimate is apt to be influenced by the given SAA frame and the estimation sometimes fails, whereas in the present approach, a reliable object image is stably reconstructed regardless of the given SAA frames.     

Real-time blind deconvolution of retinal images in adaptive optics scanning laser ophthalmoscopy

With the use of adaptive optics (AO), the ocular aberrations can be compensated to get high-resolution image of living human retina. However, the wavefront correction is not perfect due to the wavefront measure error and hardware restrictions. Thus, it is necessary to use a deconvolution algorithm to recover the retinal images. In this paper, a blind deconvolution technique called Incremental Wiener filter is used to restore the adaptive optics confocal scanning laser ophthalmoscope (AOSLO) images. The point-spread function (PSF) measured by wavefront sensor is only used as an initial value of our algorithm. We also realize the Incremental Wiener filter on graphics processing unit (GPU) in real-time. When the image size is 512 × 480 pixels, six iterations of our algorithm only spend about 10 ms. Retinal blood vessels as well as cells in retinal images are restored by our algorithm, and the PSFs are also revised. Retinal images with and without adaptive optics are both restored. The results show that Incremental Wiener filter reduces the noises and improve the image quality.     

Compact multiframe blind deconvolution

We describe a multiframe blind deconvolution (MFBD) algorithm that uses spectral ratios (the ratio of the Fourier spectra of two data frames) to model the inherent temporal signatures encoded by the observed images. In addition, by focusing on the separation of the object spectrum and system transfer functions only at spatial frequencies where the measured signal is above the noise level, we significantly reduce the number of unknowns to be determined. This "compact" MFBD yields high-quality restorations in a much shorter time than is achieved with MFBD algorithms that do not model the temporal signatures; it may also provide higher-fidelity solutions.     

Parallel blind deconvolution applied to solar image

Two parallel blind deconvolution methods, each of which uses multiple frames or several images segmented from a single frame, were applied to solar images to reconstruct their fine structure. The influence of low-contrast intensity distribution on the solar images on their reconstruction was investigated with computer simulations. The methods are also applied to an observed solar image.     

Real data results with wavelength-diverse blind deconvolution

Multiframe blind deconvolution is extended to incorporate simultaneous image acquisition at multiple wavelengths (wavelength diversity). The assumption of common path-length errors across the diversity channels allows for a parallel deconvolution procedure that exploits this coupling. No assumptions about variations in the object's intensity distribution at different wavelengths are required. The method is described and initial results for real images collected with a bench-scale imaging system are presented.     

Relative focus map estimation using blind deconvolution

An automatic focus map extraction method is presented that uses a modification of blind deconvolution for estimation of localized blurring functions. We use these local blurring functions [so-called point-spread functions (PSFs)] for extraction of focus areas on ordinary images. In this inverse task our goal is not image reconstruction but the estimation of localized PSFs and the relative focus map. Thus the method is less sensitive than general deconvolution is to noise and ill-posed deconvolution problems. The focus areas can be estimated without any knowledge of the shooting conditions or of the optical system used.     

Layered blind deconvolution using subband filter banks

A blind deconvolution method using the concept of subband filter banks has been proposed. This method makes possible the speedy convergence of solution, due to estimation of supports of the reconstructed images by a deconvolution in the lower layer. We apply the method to blind deconvolution by a simulated annealing algorithm and confirm its usefulness. Presented at 1996 International Topical Meeting on Optical Computing (OC’96), April 21–25, Sendai, Japan.     

Two-stage blind deconvolution scheme using useful priors

Hand shake blurry image is a common phenomenon in our daily life. In this paper, a novel blind deconvolution scheme is proposed to recover a single hand shake blurry image. The algorithm is subdivided into two main stages, kernel estimation stage and non-blind deconvolution stage. In the kernel estimation stage, we propose a cost function taking a selected map into consideration. In the non-blind decovolution stage, another cost function is designed using image derivatives prior. We also present an adaptive kernel size selection method instead of traditional manual selection. Extensive experiments on real world blurry images are conducted to demonstrate the performance of our algorithm.     

利用加权预测的图像迭代盲解卷积

为解决大气湍流造成的图像退化问题,本文鉴于现有的盲解卷积算法收敛性不稳定,计算量大等特点,提出了一种基于加权预测的迭代盲解卷积算法。对目前性能优秀的用迭代实现盲解卷积的L-R算法进行优化,在每次迭代结束后通过加权方法求出预测值,根据预测值计算方向加速算子,从而大大提高算法的收敛速度。实验表明：该算法不仅可对模糊退化图像进行很好的复原,同时与L-R算法相比收敛速度提高约43.8倍,其迭代速度快的特点决定了算法具有较高的工程实用价值。     

Multiframe blind image deconvolution with split Bregman method

In the paper, a multiframe blind image deconvolution method based on total variation and framelet regularizer is proposed. An adapted version of the split Bregman method is proposed to efficiently solve the resulting minimization problems. In each iteration, four sub-problems need to be solved, one of which can be very efficiently and easily solved via fast Fourier transform implementation or closed form solution. Both simulated noisy and blurred frames and real degraded frames are used to verify the effectiveness of the proposed method. Comparative experimental results show that the proposed method can efficiently remove the blur and noises and restore high quality sharp image.     

Application of multi-frame approach in single-frame blind deconvolution

A new single-frame blind deconvolution algorithm for the linear shift-invariant imaging system is presented. The algorithm processes the partial images segmented from one single degraded image by multi-frame approach to recover the point spread function (PSF). Then a deconvolution method is employed to restore the whole image with the recovered PSF. In addition, in order to improve the fidelity and resolution of the recovered PSF, the coprimeness of the partial images is utilized. Results of simulated and real atmospheric turbulence degraded images using the algorithm are reported.     

利用加权预测的图像迭代盲解卷积

为解决大气湍流造成的图像退化问题,本文鉴于现有的盲解卷积算法收敛性不稳定,计算量大等特点,提出了一种基于加权预测的迭代盲解卷积算法。对目前性能优秀的用迭代实现盲解卷积的L-R算法进行优化,在每次迭代结束后通过加权方法求出预测值,根据预测值计算方向加速算子,从而大大提高算法的收敛速度。实验表明:该算法不仅可对模糊退化图像进行很好的复原,同时与L-R算法相比收敛速度提高约43.8倍,其迭代速度快的特点决定了算法具有较高的工程实用价值。     

Constrained high-order statistical blind deconvolution of spectral data

A constrained high-order statistical algorithm is proposed to blindly deconvolute the measured spectral data and estimate the response function of the instruments simultaneously. In this algorithm, no priorknowledge is necessary except a proper length of the unit-impulse response. This length can be easily set to be the width of the narrowest spectral line by observing the measured data. The feasibility of this method has been demonstrated experimentally by the measured Raman and absorption spectral data.     

Gauss-Newton based kurtosis blind deconvolution of spectroscopic data

The spectroscopic data recorded by dispersion spectrophotometer are usually degraded by the response function of the instrument. To improve the resolving power, double or triple cascade spectrophotometer and narrow slits have been employed, but the total flux of the radiation decreases accordingly, resulting in a lower signal-to-noise ratio (SNR) and a longer measuring time. However, the spectral resolution can be improved by mathematically removing the effect of the instrument response function. Based on the ShalviWeinstein criterion, a Gauss-Newton based kurtosis blind deconvolution algorithm for spectroscopic data is proposed. Experiments with some real measured Raman spectroscopic data show that this algorithm has excellent deconvolution capability.     

数字图像消卷积在显微测量中的应用

研究了数字图像消卷积在显微测量中的应用。在测量系统点扩展函数的基础上,利用频谱空间变换消卷积图像处理方法,消除了传统光学显微成像中的几何不清晰度。通过对显微图像进行复原,实现了反射式一维线宽的显微精确测量。在医用接骨钢板表面质量的评价中,对10μm量级的划痕宽度实现了精确和方便的测量。该技术对相关计量技术的发展具有重要意义。