Determination of optical transfer function by inspection of frequency-domain plot

Gennery, Donald B.

doi:10.1364/josa.63.001571

Cited by 95 publications

(28 citation statements)

References 4 publications

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“…It should be noted that, for the noisy case, results for average values of Gaussian blur estimates over 100 independent trials have been plotted against the actual values under different noise levels. Next, an image was blurred using uniform motion blur in one direction, as proposed in [3], and blur parameters were estimated using low rank approximation of cepstrum. These motion blur parameters are calculated using (9) after 'properly' thresholding the low rank approximations.…”

Section: Resultsmentioning

confidence: 99%

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Blind Blur Estimation Using Low Rank Approximation of Cepstrum

Bhutta

Foroosh

2006

Lecture Notes in Computer Science

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Abstract. The quality of image restoration from degraded images is highly dependent upon a reliable estimate of blur. This paper proposes a blind blur estimation technique based on the low rank approximation of cepstrum. The key idea that this paper presents is that the blur functions usually have low ranks when compared with ranks of real images and can be estimated from cepstrum of degraded images. We extend this idea and propose a general framework for estimation of any type of blur. We show that the proposed technique can correctly estimate commonly used blur types both in noiseless and noisy cases. Experimental results for a wide variety of conditions i.e., when images have low resolution, large blur support, and low signal-to-noise ratio, have been presented to validate our proposed method.

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Section: Resultsmentioning

confidence: 99%

“…Numerous techniques [1] have been proposed over the years which try to estimate Point Spread Function (PSF) of blur either separately from the image [2,3,4,5], or simultaneously with the image [6,7]. The first set of these techniques, also known as Fourier-based techniques, normally use the idea of finding zeros in the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

Blind Blur Estimation Using Low Rank Approximation of Cepstrum

Bhutta

Foroosh

2006

Lecture Notes in Computer Science

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“…It is known that point spread functions for such blurs do have zeros in the frequency domain, and they can be uniquely identified by the location of these zero crossings (Cannon, 1976;Gennery, 1973). If the original image is blurred and noiseless then the blur matrix A in Eq.…”

Section: Restoration Of Uniformly Blurred Image With Spatial Variationsmentioning

confidence: 99%

“…Certain types of distortion operators are completely characterized by attributes such as the location of frequency-domain zeros. The techniques (Cannon, 1976;Gennery, 1973) make the following two assumptions: (i) the blurring produces zero crossings in the frequency domain and it can be completely characterized by the location of these zero crossings, and (ii) the location of zero crossings can be determined from the Fourier transform or power cepstrum (the logarithm of the power spectrum) of the observed image. These methods are very simple to use and they can successfully applied in many real-life situations.…”

Section: Identification Of Degradation Operators and Estimation Parammentioning

confidence: 99%

Image Restoration Using Two-Dimensional Variations

Milukova¹,

Kober²,

Karnaukhov³

2012

Image Restoration - Recent Advances and Applications

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“…Optimal values for the θ and υ parameters have been investigated in [4]. These parameters determine the "shape" of the influence function and as a consequence the filtering of outliers.…”

Section: Robust Functionals To the Restoration Cost Functionmentioning

confidence: 99%

Enhancement of multiple sensor images using joint image fusion and blind restoration

Mitianoudis

Stathaki

2008

Image Fusion

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Image fusion systems aim at transferring "interesting" information from the input sensor images to the fused image. The common assumption for most fusion approaches is the existence of a high-quality reference image signal for all image parts in all input sensor images. In the case that there are common degraded areas in at least one of the input images, the fusion algorithms can not improve the information provided there, but simply convey a combination of this degraded information to the output. In this study, the authors propose a combined spatial-domain method of fusion and restoration in order to identify these common degraded areas in the fused image and use a regularised restoration approach to enhance the content in these areas. The proposed approach was tested on both multi-focus and multi-modal image sets and produced interesting results.

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Determination of optical transfer function by inspection of frequency-domain plot

Cited by 95 publications

References 4 publications

Blind Blur Estimation Using Low Rank Approximation of Cepstrum

Blind Blur Estimation Using Low Rank Approximation of Cepstrum

Image Restoration Using Two-Dimensional Variations

Enhancement of multiple sensor images using joint image fusion and blind restoration

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