scholarly journals Image Deconvolution by Means of Frequency Blur Invariant Concept

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Barmak Honarvar Shakibaei ◽  
Peyman Jahanshahi
Keyword(s):  
Point Spread Function ◽  
Fourier Transforms ◽  
Spatial Domain ◽  
Image Deconvolution ◽  
Original Image ◽  
Invariant Features ◽  
Point Spread ◽  
Spread Function ◽  
Degraded Image ◽  
The Moment

Different blur invariant descriptors have been proposed so far, which are either in the spatial domain or based on the properties available in the moment domain. In this paper, a frequency framework is proposed to develop blur invariant features that are used to deconvolve a degraded image caused by a Gaussian blur. These descriptors are obtained by establishing an equivalent relationship between the normalized Fourier transforms of the blurred and original images, both normalized by their respective fixed frequencies set to one. Advantage of using the proposed invariant descriptors is that it is possible to estimate both the point spread function (PSF) and the original image. The performance of frequency invariants will be demonstrated through experiments. An image deconvolution is done as an additional application to verify the proposed blur invariant features.

scholarly journals Estimating the Size of the Point Spread Function Using Fuzzy Logic for Blind Restoration of Satellite Image

2017 ◽  
Vol 5 (4) ◽  
pp. 48
Author(s):  
Fouad Aouinti ◽  
M'barek Nasri ◽  
Mimoun Moussaoui
Keyword(s):  
Point Spread Function ◽  
Fuzzy Inference ◽  
Blind Deconvolution ◽  
Satellite Image ◽  
Inference System ◽  
Point Spread ◽  
Spread Function ◽  
Degraded Image ◽  
Blind Restoration ◽  
Image Edges

Despite the considerable progress in the field of imaging, the acquired image can undergo certain degradations which are mainly summarized in blur and noise. The objective of the restoration is to estimate from the observed image an image as close as possible to the original image. The iterative blind deconvolution (IBD) can be used effectively when no information about the distortion is known. This algorithm starts with a random initial estimate of the point spread function (PSF) whose its size affects strongly the restoration process of the degraded image. In this paper, we have implemented a fuzzy inference system (FIS) to determine the size of the PSF through the examination of the blurred satellite image edges and the measurement of the blur width in pixels around an obviously sharp object. The obtained results are encouraging, which confirms the good performance of the proposed approach.

Wiener Filter Based on the Image to Blur

2012 ◽  
Vol 591-593 ◽  
pp. 1567-1570
Author(s):  
Chao Da Chen ◽  
Si Qing Zhang ◽  
Chui Xin Chen
Keyword(s):  
Image Quality ◽  
Image Restoration ◽  
Point Spread Function ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Point Spread ◽  
Spread Function ◽  
Degraded Image ◽  
Ill Posed ◽  
Fuzzy Image

Image restoration, refers to the removal or loss in the process of getting digital image degradation of the image quality, image restoration technology is the key to meet the requirements of the point spread function, degradation model is an ill-posed integral equations, in the frequency domain, when H ( U, V ) less or equal to zero, the noise will be amplified, the degraded image and interference in H ( U, V ) value of the spectrum will be small to restore the image influence. In view of the point spread function put forward Wiener filtering algorithm, the noise lead to ill-posed integral with specified signal-to-noise ratio to reduce image restoration effects, through the IPT toolbox for fuzzy image restoration, image quality to achieve the anticipated effect.

scholarly journals Iterative Blind Deconvolution Algorithm for Deblurring a Single PSP/TSP Image of Rotating Surfaces

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3075 ◽  
Author(s):  
Anshuman Pandey ◽  
James Gregory
Keyword(s):  
Time Constant ◽  
Point Spread Function ◽  
Computational Time ◽  
Image Deconvolution ◽  
Pressure Gradients ◽  
Local Pressure ◽  
Experimental Conditions ◽  
Excitation Light ◽  
Point Spread ◽  
Spread Function

Imaging of pressure-sensitive paint (PSP) for pressure measurement on moving surfaces is problematic due to the movement of the object within the finite exposure time of the imager, resulting in the blurring of the blade edges. The blurring problem is particularly challenging when high-sensitivity PSP with a long lifetime is used, where the long luminescence time constant of exponential light decay following a burst of excitation light energy results in blurred images. One method to ameliorate this effect is image deconvolution using a point spread function (PSF) based on an estimation of the luminescent time constant. Prior implementations of image deconvolution for PSP deblurring have relied upon a spatially invariant time constant in order to reduce computational time. However, the use of an assumed value of time constant leads to errors in the point spread function, particularly when strong pressure gradients (which cause strong spatial gradients in the decay time constant) are involved. This work introduces an iterative method of image deconvolution, where a spatially variant PSF is used. The point-by-point PSF values are found in an iterative manner, since the time constant depends on the local pressure value, which can only be found from the reduced PSP data. The scheme estimates a super-resolved spatially varying blur kernel with sub-pixel resolution without filtering the blurred image, and then restores the image using classical iterative regularization tools. A kernel-free forward model has been used to generate test images with known pressure surface maps and a varying amount of noise to evaluate the applicability of this scheme in different experimental conditions. A spinning disk setup with a grazing nitrogen jet for producing strong pressure gradients has also been used to evaluate the scheme on a real-world problem. Results including the convergence history and the effect of a regularization-iteration count are shown, along with a comparison with the previous PSP deblurring method.

A Turbulence-Degraded Image Restoration Algorithm Using the Maximum Likelihood Estimation Method

2014 ◽  
Vol 543-547 ◽  
pp. 2391-2394
Author(s):  
Feng Wang ◽  
Kun Fan Zhang ◽  
Fan Kun Meng ◽  
Yong Jun Zhao
Keyword(s):  
Point Spread Function ◽  
Short Exposure ◽  
Computational Formula ◽  
Object Image ◽  
Point Spread ◽  
Restoration Algorithm ◽  
Spread Function ◽  
Degraded Image ◽  
Numerical Computing ◽  
Degraded Images

The RL(Richardson-Lucy) algorithm is an important method for restoration of turbulence-degraded images. However, the shortcoming of this method is that it tends to amplify the noise and exsits excessive smoothing in the iterative procedure. This paper discusses the RL algorithm and its improving methods focusing on turbulence-degraded images restoration.Firstly, a short exposure atmospheric turbulence-degraded model is established and a numerical computing method is proposed for random phase screen. Secondly, the essential principle and computational formula are deduced. To restore the object image effectively from the turbulence-degraded image, a new double-circulation iterative Richardson-Lucy restoration algorithm using TV-regularized method is proposed. This new algorithm introduces the total variation restraint and estimates the object image and the point spread function based on the inner and outer double-circulation iteration, which can use the inherent relation between the object image and the point spread function adequately. Simulation experiments show that the proposed algorithm can effectively preserve the details and edges of the image and its restoration effect is obviously better than the traditional RL algorithm.

scholarly journals Sensitivity to point-spread function parameters in medical ultrasound image deconvolution

Ultrasonics ◽  
2009 ◽  
Vol 49 (3) ◽  
pp. 344-357 ◽  
Author(s):  
Ho-Chul Shin ◽  
Richard Prager ◽  
James Ng ◽  
Henry Gomersall ◽  
Nick Kingsbury ◽  
...  
Keyword(s):  
Point Spread Function ◽  
Ultrasound Image ◽  
Medical Ultrasound ◽  
Image Deconvolution ◽  
Point Spread ◽  
Spread Function
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