Spread Function Estimation Research Articles

On-orbit spatial characterization based on satellite image structure

One practical way to characterize the spatial performance of an in-flight satellite optical imaging system is to determine the modulation transfer function (MTF) from its remotely sensed images on the ground. Currently, there are many MTF measurement techniques designed to provide accurate measurements for high-resolution imaging systems. However, these existing techniques usually rely on the presence and manual identification of a well-separated characteristics target. We introduce an insight to effectively estimate a degradation function based on the MTF by analyzing the nonlocal self-similarity characteristics, namely the structural component, in the observed image. To this end, this paper presents several strategies to realize the aim of this work. First, we propose a segmentation method to select the ideal candidates for MTF estimation. Second, we develop an adaptive structure selection method that removes detrimental structures and selects useful information for point spread function (PSF) estimation. Finally, we put forward a robust estimation method by introducing a spatial prior that is able to simultaneously suppress noises while preserving the sparsity and continuity of the PSF kernels. For the experiments, datasets used in the analyses are synthetically blurred images simulated from level 2A product of IKONOS and real remotely sensed images from level 0 product of RazakSAT. The experimental results demonstrate that the proposed method is practical and effective, with <2.3 % of relative error at the Nyquist frequency as compared to the well-established edge method. This indicates that the proposed method can warrant a reliable result for on-orbit spatial characterization.

Parametric PSF estimation based on predicted-SURE with $$\ell _1$$ ℓ 1 -penalized sparse deconvolution

Point spread function (PSF) estimation plays an important role in blind image deconvolution. It has been shown that incorporating Wiener filter, minimization of the predicted Stein’s unbiased risk estimate (p-SURE)—unbiased estimate of predicted mean squared error—could yield an accurate PSF estimate. In this paper, we provide a theoretical analysis for the PSF estimation error, which shows that the better deconvolution leads to more accurate PSF estimate. It motivates us to incorporate an \(\ell _1\)-penalized sparse deconvolution into the p-SURE minimization, instead of the Wiener-type filtering. In particular, based on FISTA—one of the most popular iterative \(\ell _1\)-solvers, we evaluate the p-SURE for each update, by Jacobian recursion and Monte Carlo simulation. Numerical results of both synthetic and real experiments demonstrate the improvements in PSF estimate, and therefore, deconvolution performance.

SURE-Type Functionals as Criteria for Parametric PSF Estimation

Point spread function (PSF) estimation plays an important role in blind image deconvolution. This paper proposes two novel criteria for parametric PSF estimation, based on Stein's unbiased risk estimate (SURE), namely, prediction-SURE and its variant. We theoretically prove the SURE-type functionals incorporating exact (complementary) smoother filtering as the valid criteria for PSF estimation. We also provide the theoretical error analysis for the regularizer approximations, by which we show that the proposed frequency-adaptive regularization term yields more accurate PSF estimate than others. In particular, the proposed SURE-variant enables us to avoid estimation of noise variance, which is a key advantage over the traditional SURE-like functional. Finally, we propose an efficient algorithm for the minimizations of the criteria. Not limited to the examples we show in this paper, the proposed SURE-based framework has a great potential for other imaging applications, provided the parametric PSF form is available.

Method for Large-Area Satellite Image Quality Enhancement With Local Aerial Images Based on Non-Target Multi-Point Calibration

This paper designed a non-target multi-point calibration method for the quality enhancement of large-area satellite images by using local aerial images. Satellite images are more sensitive to atmospheric effects compared with aerial images. Atmospheric effects on aerial images are even negligible in fine weather. Given that aerial remote sensing has high spatial resolution and geometric fidelity, more spatial details can be recorded in aerial images. However, the scan bandwidth of aerial images is limited compared with that of satellite images. Thus, taking high-quality aerial images of a neighborhood as reference can provide prior knowledge for point spread function (PSF) estimation and for the quality enhancement of large-area satellite images. The least square method and interpolation are used for the PSF estimation of spatial variation, and then total variation minimization is used for recovery. The results show that the designed method can effectively enhance the quality of large-area satellite images.

Sub-pixel discrete method of point spread function from blurred images

Fast and accurate Point Spread Function(PSF) estimation method is the premise to obtain good results on the blur image restoration.To solve the deficiency of the discrete PSF of defocus-blurred and motion-blurred images,a discretization method was proposed based on the combination of geometric property of degradation model and sub-pixel estimation.Specifically,the principle of weight allocation was defined,which was related to the distance with the neighboring pixels.Thus,the discretization of PSF was realized.Finally,the experimental results illustrate that the proposed method improves result precision and outperforms the traditional one on visual quality,sharpness evaluation function,Peak Signal-to-Noise Ratio(PSNR) and Improved Signal-to-Noise Ratio(ISNR).

PSF Estimation and Image Restoration for Motion Blurred Images

Point spread function (PSF) estimation and image restoration algorithm are the hotspots In the research of motion blurred image restoration. In order to improve the efficacy of image restoration, an improved algorithm named quadric transforms (QT) method is proposed in this paper by analyzing the restoration process of motion blurred images. Firstly, Fourier transform and homomorphism transform are applied to the original motion blurred image, and then the Fourier transform and homomorphism transform are used again to the obtained spectrum image. Secondly, the motion blur direction is estimated by Radon transform. Thirdly, the motion blur length is found by differential autocorrelation operations. Finally, utilizing the estimated blur direction and blur length, the motion blurred image is restored by Wiener filtering. Experimental results show that the proposed QT method can get more accurate estimated motion blur angles than the primary transform (PT, that is, Fourier transform and homomorphism transform are used one time) method and can get better restored images under the meaning of peak signal to noise ratio (PSNR).

Joint Super-Resolution and segmentation from a set of Low Resolution images using a Bayesian approach with a Gauss-Markov-Potts Prior

This paper addresses the problem of creating a Super-Resolution (SR) image from a set of Low Resolution (LR) images. SR image reconstruction can be viewed as a three-task process: registration or motion estimation, Point Spread Function (PSF) estimation and High Resolution (HR) image reconstruction. In the current work, we propose a new method based on the Bayesian estimation with a Gauss-Markov-Potts Prior Model (GMPPM) where the main objective is to get a new HR image from a set of severely blurred, noisy, rotated and shifted LR images. As a by-product of our prior model, we obtain jointly an SR image and an optimal segmentation of it. The proposed algorithm is unsupervised. A comparison of the performances of the proposed method with some classical and recent SR methods is provided in simulation.

Unified regularization framework for blind image super-resolution

Blind superresolution (BSR) is one of the challenges in image superresolution. We propose a new approach using a unified regularization framework, which solves image registration, point spread function (PSF) estimation, and high-resolution (HR) image reconstruction simultaneously. To achieve this, the anisotropic diffusion techniques are employed as one regularization term to preserve edge information in the HR image estimation, and a generalized version of the eigenvector-based (EVAM) constraint is developed to regularize the PSF. An alternating minimization algorithm is devised to find optimal solutions, and an effective numerical implementation scheme, based on local filtering, is proposed to suppress the ringing artifacts in the image reconstruction. Finally, experiments with synthetic and real data are presented to demonstrate the effectiveness and robustness of our approach, which can handle motion blur well and enhance resolution notably for very noisy images.

A study of coincidence line spread function (CLSF) estimation for small scintillators using quadrant sharing technique

Inter-crystal scatter is one of the primary reasons to misplace the coincidence events in positron emission tomography (PET). The probability of Compton scattering of a 511 KeV photon within the crystal is more than 50% of all interactions. Unlike one-to-one scintillator-photomultiplier tube (PMT) coupled (discrete) detectors, detector blocks utilizing the light sharing technique must have an additional degradation on their spatial resolution due to shared scintillation photons within the block. This study focuses on an estimation of the coincidence line spread function (CLSF) for 7/spl times/7 BGO (bismuth germanate) detector blocks by comparing the simulation results for discrete and light shared detectors. Based on experimental measurements, decoding probabilities, which describe the uncertainty in positioning between neighboring crystals, were estimated from photoelectron statistics. The results were embedded into the Monte Carlo simulation to estimate the CLSF more accurately. In this study, two 7/spl times/7 BGO detector blocks (2.68 mm/spl times/2.68 mm/spl times/18 mm) with different surface treatments coupled to 4 PMTs were used to estimate the decoding probability values. For discrete detectors, the FWHM values of CLSF for 7/spl times/7 BGO block with crystal dimensions of 2.68 mm/spl times/2.68 mm/spl times/18 mm were approximately found 1.47 mm for discrete detectors and 1.51 mm for light shared detectors. The intrinsic spatial resolutions of 7/spl times/7 light shared BGO block with crystal dimensions of 2.68 mm/spl times/2.68 mm/spl times/18 mm for 20 cm, 40 cm and 80 cm detector ring diameters were projected to 1.57 mm, 1.75 and 2.30 mm, respectively. Making the assumption that LSO (lutetium orthosilicate) crystals have 5 times more light output than BGO, 14/spl times/14 LSO block with crystal dimensions of 1.32 mm/spl times/1.32 mm/spl times/20 mm can be decoded and intrinsic spatial resolutions for 20 cm, 40 cm and 80 cm detector ring diameters were expected to be 0.87 mm, 1.15 mm and 1.91 mm, respectively.