Point Spread Function Estimation Research Articles

Jitter Detection and Image Restoration Based on Continue Dynamic Shooting Model for High-Resolution TDI CCD Satellite Images

Although time delay integration charge-coupled devices (TDI CCDs) have been widely used in high-resolution spaceborne optical cameras, they are sensitive to satellite jitter: the images obtained by them are affected by both distortion and blur. Therefore, according to the multistage integral imaging characteristics of TDI CCDs, this article not only proposes a continue dynamic shooting model (CDSM) to reflect the real push-broom mode of the satellite but also presents a method containing jitter detection and image restoration based on it. In the presented method, the CDSM subdivides the TDI CCD integration intervals. The subdivision number of CDSM is determined by the proposed integral transformation function (ITF). Then, it feeds back into the ITF and also contributes to the point spread function (PSF) estimation. Among the abovementioned, ITF defines the relationship between the parallax images and the jitter curve, and aims to improve the jitter detection performance. Finally, an adaptive image restoration based on context is conducted, which combines time, space, and spectrum information. Besides the simulated images, multispectral images of GaoFen-1 02 satellite were also adopted to validate the performance of the presented method. Experimental results indicate that the accuracy of the jitter detection is increased, and the geometric and radiometric qualities of restored images are also improved.

Image recovery of ghost imaging with sparse spatial frequencies.

When the spatial frequencies of the object are insufficiently sampled, the reconstruction of ghost imaging will suffer from repetitive visual artifacts, which cannot be effectively tackled by existing ghost imaging reconstruction techniques. In this Letter, extensions of the CLEAN algorithm applied in ghost imaging are explored to eliminate those artifacts. Combined with the point spread function estimation using the second-order coherence measurement in ghost imaging, our modified CLEAN algorithm is demonstrated to have a fast and noteworthy improvement against the spatial-frequency insufficiency, even for the extreme sparse sampling cases. A brief explanation of the algorithm and performance analysis are given.

Parametric PSF estimation based on recursive SURE for sparse deconvolution

Abstract PSF (point spread function) estimation plays an important role in blind image deconvolution. It has been shown in our previous work that minimization of the Stein’s unbiased risk estimate (SURE) – unbiased estimate of mean squared error (MSE) – could yield an accurate PSF estimate. In this paper, we show that the PSF estimation error is upper bounded by the deconvolution accuracy and the mismatch between the assumed PSF parametric form and the underlying true one. For this reason, we incorporate the ℓ 1 {\ell_{1}} -penalized sparse deconvolution into the SURE instead of previously used Wiener filter. In particular, we apply the iterative soft-thresholding algorithms to solve ℓ 1 {\ell_{1}} -minimization, and develop recursive evaluations of SURE, which is then shown to converge to the existing theoretical result. In practical implementations with large-scale data, we apply the Monte-Carlo simulation to avoid the explicit matrix operation. Numerical examples demonstrate the improvements of PSF estimate, and the resulting deconvolution performance.

Improved estimation of motion blur parameters for restoration from a single image.

This paper presents an improved method to estimate the blur parameters of motion deblurring algorithm for single image restoration based on the point spread function (PSF) in frequency spectrum. We then introduce a modification to the Radon transform in the blur angle estimation scheme with our proposed difference value vs angle curve. Subsequently, the auto-correlation matrix is employed to estimate the blur angle by measuring the distance between the conjugated-correlated troughs. Finally, we evaluate the accuracy, robustness and time efficiency of our proposed method with the existing algorithms on the public benchmarks and the natural real motion blurred images. The experimental results demonstrate that the proposed PSF estimation scheme not only could obtain a higher accuracy for the blur angle and blur length, but also demonstrate stronger robustness and higher time efficiency under different circumstances.

The effect of the point spread function on downscaling continua

The point spread function (PSF) is ubiquitous in remote sensing. This paper investigated the effect of the PSF on the downscaling of continua. Geostatistical approaches were adopted to incorporate explicitly, and reduce the influence of, the PSF effect in downscaling. Two general cases were considered: univariate and multivariate. In the univariate case, the input coarse spatial resolution image is the only image available for downscaling. Area-to-point kriging was demonstrated to be a suitable solution in this case. For the multivariate case, a finer spatial resolution image (or images) observed under different conditions (e.g., at a different wavelength) is available as auxiliary data for downscaling. Area-to-point regression kriging was shown to be a suitable solution for this case. Moreover, a new solution was developed for estimating the PSF in image scale transformation. The experiments show that the PSF effect influences downscaling greatly and that downscaling can be enhanced obviously by considering the PSF effect through the geostatistical approaches and the PSF estimation solution proposed.

Blind Poissonian Image Deblurring Regularized by a Denoiser Constraint and Deep Image Prior

The denoising and deblurring of Poisson images are opposite inverse problems. Single image deblurring methods are sensitive to image noise. A single noise filter can effectively remove noise in advance, but it also damages blurred information. To simultaneously solve the denoising and deblurring of Poissonian images better, we learn the implicit deep image prior from a single degraded image and use the denoiser as a regularization term to constrain the latent clear image. Combined with the explicit L0 regularization prior of the image, the denoising and deblurring model of the Poisson image is established. Then, the split Bregman iteration strategy is used to optimize the point spread function estimation and latent clear image estimation. The experimental results demonstrate that the proposed method achieves good restoration results on a series of simulated and real blurred images with Poisson noise.

Iterative approach for parametric PSF estimation

In this paper a parametric point spread function (PSF) estimation method is presented. This method can be employed for estimating the parameters of linear motion blur vector. The proposed method works on single image and estimates angle and length of motion blur vector to generate required PSF for deblurring. This method is based on step-by-step estimation of motion blur vector. In the first approximation the blind method considers short length vectors in all directions and deblurs image with PSF of these candidate vectors. The quality of deblurred image is assessed by a no-reference quality measurement metric which is proposed in this paper. The proposed no-reference image quality metric evaluates degradation in sharpness of edges and the amount of resulted artifact caused by saturated pixels, in the deblurred image. Those motion vectors that caused unacceptable deblurring results are omitted in the next iteration. The approximation is improved by increasing the length of the remaining vectors and the same process continues iteratively. The process goes on until only one vector remains as the estimation for motion blur vector. Experimental results show that in estimation of motion blur vectors, the length estimation error is less than one pixel in 85% of the cases and angle estimation error in 95% of the cases is less than one degree. Comparing with a conventional method indicates that the proposed method shows more than four times improvement in length estimation and 10% improvement in angle estimation. Moreover, the proposed method has comparatively lower computational load than other conventional deblurring methods.

Arbitrarily shaped Point Spread Function (PSF) estimation for single image blind deblurring

The research paper focuses on a challenging task faced in blind image deblurring (BID). It relates to the estimation of arbitrarily shaped (nonparametric or complex shaped) point spread functions (PSFs) of motion blur caused by camera handshake. These PSFs exhibit much more complex shapes than their parametric counterparts and deblurring, in this case, requires intricate ways to estimate the blur and effectively remove it. This research work introduces a novel blind deblurring scheme visualized for deblurring images corrupted by arbitrarily shaped PSFs. It is based on genetic algorithm and utilizes the Blind/Reference-less Image Spatial QUality Evaluator (BRISQUE) measure as the fitness function for arbitrarily shaped PSF estimation. The proposed BID scheme has been compared with other state-of-the-art single image motion deblurring schemes as benchmarks. Validation has been carried out on the standard real-life blurred images. Results of both benchmark and real images are presented. For real-life blurred images, the proposed BID scheme using BRISQUE converges in close vicinity of the original blurring functions. However, the benchmark schemes fail to effectively restore the real blurred images. The proposed scheme surpasses on average of seven percent higher image quality as compared to the benchmark schemes.

Blind deconvolution in astronomy with adaptive optics: the parametric marginal approach

ABSTRACT One of the major limitations of using adaptive optics (AO) to correct image post-processing is the lack of knowledge about the system’s point spread function (PSF). The PSF is not always available as direct imaging on isolated point-like objects, such as stars. The use of AO telemetry to predict the PSF also suffers from serious limitations and requires complex and yet not fully operational algorithms. A very attractive solution is to estimate the PSF directly from the scientific images themselves, using blind or myopic post-processing approaches. We demonstrate that such approaches suffer from severe limitations when a joint restitution of object and PSF parameters is performed. As an alternative, here we propose a marginalized PSF identification that overcomes this limitation. In this case, the PSF is used for image post-processing. Here we focus on deconvolution, a post-processing technique to restore the object, given the image and the PSF. We show that the PSF estimated by marginalization provides good-quality deconvolution. The full process of marginalized PSF estimation and deconvolution constitutes a successful blind deconvolution technique. It is tested on simulated data to measure its performance. It is also tested on experimental AO images of the asteroid 4-Vesta taken by the Spectro-Polarimetric High-contrast Exoplanet Research (SPHERE)/Zurich Imaging Polarimeter (Zimpol) on the Very Large Telescope to demonstrate application to on-sky data.

Estimation of point spread function of an optical microscope using stochastic minimization of least square errors

In this paper we propose a point spread function (PSF) estimation scheme for an optical microscope. The scheme is based on minimizing the least square error between the image of an arbitrary target and the convolution of a stochastically constructed PSF array with the geometrical-optics predicted image of the target. The proposed scheme is independent of the geometry of the imaging system and will work in any type of optical microscopes. The scheme enables PSF estimation without requiring any modification of the microscope setup. We have demonstrated the proposed scheme both numerically and experimentally.

Super-resolution Scanning Electrochemical Microscopy

To achieve super-resolution scanning electrochemical microscopy (SECM), we must overcome the theoretical limitation associated with non-contact electrochemical imaging of surface-generated species. This is the requirement for mass transfer to the electrode, which gives rise to the diffusional broadening of surface features. In this work, a procedure is developed for overcoming this limitation and thus generating ``super-resolved" images using point spread function (PSF)-based deconvolution, where the point conductor plays the same role as the point emitter in optical imaging. In contrast to previous efforts in SECM towards this goal, our method uses a finite element model to generate a pair of corresponding blurred and sharp images for PSF estimation, avoiding the need to perform parameter optimization for effective deconvolution. It can therefore be used for retroactive data treatment and enhanced understanding of the structure-property relationships that SECM provides.

Nonuniformity Correction of Resistance Array Based on Estimation of Point Spread Function

Nonuniformity Correction of Resistance Array Based on Estimation of Point Spread Function

Deep Super-Resolution Imaging Technology: Toward Optical Super-Vision

Spatially variant optical blur is inevitable in real optical imaging systems with imperfect camera lenses since each object has a different depth in a real scene. Unfortunately, existing super-resolution imaging techniques may not achieve satisfactory performance on images acquired from practical optical imaging systems, where the optical blur is unknown. This is because a predefined global point spread function is adopted for every spatial coordinate without any optical blur estimation in the existing models. To address the challenging issue in recent video technologies, in this article, we propose an optimal super-resolution imaging network based on object-adaptive optical point spread function estimation. In technical, a new object sharpness consistency cost is suggested in the dark channel domain to estimate the point spread function, which is optimal to the super-resolution imaging. In addition, an object image sharpening network is proposed and adaptively combined with the point spread function estimation. The performance of the proposed network is also validated via experiments in terms of various quantitative metrics such as the peak signal-to-noise ratio, S3 index, and BRISQUE values.

Modified Conditional Generative Adversarial Network-Based Optical Blur Restoration for Finger-Vein Recognition

Among the existing biometrics methods, finger-vein recognition is beneficial because finger-veins patterns are locate under the skin and thus difficult to forge. Moreover, user convenience is high because non-invasive image capturing devices are used for recognition. In real environments, however, optical blur can occur while capturing finger-vein images du to both skin scattering blur caused by light scattering in the skin layer and lens focus mismatch caused by finger movement. The blurred images generated in this manner can cause severe performance degradation for finger-vein recognition. The majority of the previous studies addressed the restoration method o skin scattering blurred images; however, only limited studies have addressed the restoration of optically blurred images. Even the previous studies on the restoration of optical blur restoration have performed restoration based on the estimation of the accurate point spread function (PSF) for a specific image-capturing device. Thus, it is difficult to apply these methods to finger-vein images acquired by different devices. To address this problem, this paperproposes a new method for restoring optically blurred finger-vei images using a modified conditional generative adversarial network (conditional GAN) and recognizing the restored finger-vein images using a deep convolutional neural network (CNN). The results of the experiment performed using two open databases, the Shandong University homologous multimodal traits (SDUMLA-HMT) finger-vein database and Hong Kong Polytechnic University finger-image database (version 1) confirmed that the proposed method outperforms the existing methods.

Adaptive Optics Image Restoration via Regularization Priors With Gaussian Statistics

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.

Point-spread Function Estimation for Adaptive Optics Imaging of Astronomical Extended Objects

Abstract This paper focuses on point-spread function (PSF) estimation for astronomical images containing only single extended objects captured by adaptive optics systems. The problem is very different from, and much more challenging than, PSF estimation with point-like source images. We propose a new PSF estimation framework based on deep-learning technology. In this framework, PSFs can be estimated “end-to-end” using the original degraded images. Moreover, such a framework can precisely address different sources of blur without requiring accurate prior information about the PSF, image or imaging system. Therefore, the method is practical. We test the proposed method on both simulated and real data, and the favorable results show that the method is valid and performs much better than classical methods do.

Self-calibrating wave-encoded 3D turbo spin echo imaging using subspace model based autofocusing.

To develop a self-calibrating approach for the estimation of wave point spread function (PSF) and coil sensitivities from the subsampled wave-encoded k-space, and evaluate its performance for wave-encoded 3D turbo spin echo (TSE) imaging. A low rank subspace parametric model was demonstrated in simulation to improve the representation for practical wave encoding k-space trajectories with aperiodicity, and an autofocus metric for the entire imaging volume was used to calibrate the wave PSF in a 2-stage manner from coarse to refined estimation. The coil sensitivities can be extracted from the shifted central region of wave PSF corrected subsampled k-space, and further used with wave PSF for wave-encoded parallel imaging (PI) reconstruction. The wave encoding gradients were integrated into the 3D TSE sequence considering eddy current reduction aspects and maintaining of the Carr-Purcell-Meiboom-Gill condition. Phantom and in vivo brain experiments were performed to evaluate the accuracy of wave PSF self-calibration and to compare the PI reconstruction performance between wave and Cartesian encoding scheme. The self-calibrated wave PSF, estimated from different k-space undersampling patterns can robustly correct the wave encoding induced image artifacts given sufficient central autocalibration data. The self-calibrating wave-encoded PI reconstruction has demonstrated its improved performance in reduced aliasing artifacts and noise amplification in comparison to the Cartesian-encoded PI reconstruction results for 3D TSE imaging. The proposed self-calibrating wave-encoded method allows robust calibration of wave PSF and coil sensitivities from the subsampled k-space, and improves the overall image quality for accelerated 3D TSE imaging.

Deep Self-Learning Network for Adaptive Pansharpening

Deep learning (DL)-based paradigms have recently made many advances in image pansharpening. However, most of the existing methods directly downscale the multispectral (MSI) and panchromatic (PAN) images with default blur kernel to construct the training set, which will lead to the deteriorative results when the real image does not obey this degradation. In this paper, a deep self-learning (DSL) network is proposed for adaptive image pansharpening. First, rather than using the fixed blur kernel, a point spread function (PSF) estimation algorithm is proposed to obtain the blur kernel of the MSI. Second, an edge-detection-based pixel-to-pixel image registration method is designed to recover the local misalignments between MSI and PAN. Third, the original data is downscaled by the estimated PSF and the pansharpening network is trained in the down-sampled domain. The high-resolution result can be finally predicted by the trained DSL network using the original MSI and PAN. Extensive experiments on three images collected by different satellites prove the superiority of our DSL technique, compared with some state-of-the-art approaches.

Microscopic imaging quality improvement through L0 gradient constraint model based on multi-fields of view analysis

The degradation of optical microscopic imaging is space-variant, and how to fast restore optical degraded image remains a special problem. Based on point spread function (PSF) estimation under each field of view (FOV), a L0 gradient-constrained image restoration method is proposed to solve optical degradation in microscopic imaging. Firstly, the whole scene is segmented into several different regions according to different FOV. The PSFs for each region are estimated from modulation transfer function (MTF) measured in advance. Secondly, a penalty function is designed using L0 gradient constraint to deblur the degraded images of each sub-FOV. Finally, a weighted stitching approach is used to stitch the restored images of multiple FOV (m-FOV). Experimental results indicate that the m-FOV analysis could well solve the problem of space-variant degradation. Compared with the other methods, both subjective and objective evaluation results prove that the L0 norm idea could rapidly and effectively restore the degraded image. The approach could be well applied to a real product.

PRIME: PSF Reconstruction and Identification for Multiple-source characterization Enhancement – application to Keck NIRC2 imager

ABSTRACT In order to enhance the scientific exploitation of adaptive optics (AO)-assisted observations, we investigate a novel hybrid concept to improve the parametric estimation of point spread function (PSF) called PSF Reconstruction and Identification for Multiple-source characterization Enhancement (PRIME). PRIME uses both focal and pupil-plane measurements to estimate jointly the model parameters related to the atmosphere [$C_n^2(h)$, seeing] and the AO system (e.g. optical gains and residual low-order errors). Photometry and astrometry are provided as by-products. The parametric model in use is flexible enough to be scaled with field location and wavelength, making it a proper choice for optimized on-axis and off-axis data-reduction across the spectrum. Here, we present the methodology and validate PRIME on engineering and binary Keck II telescope NIRC2 images. We also present applications of PSF model parameters retrieval using PRIME: (i) calibrate the PSF model for observations void of stars on the acquired images, i.e. optimize the PSF reconstruction process, (ii) update the AO error breakdown mutually constrained by the telemetry and the images in order to speculate on the origin of the missing error terms and evaluate their magnitude, and (iii) measure photometry and astrometry with an application to the triple system Gl569 images.