Sharpness measure: towards automatic image enhancement

Shaked, Doron; Tastl, Ingeborg

doi:10.1109/icip.2005.1529906

Cited by 76 publications

(46 citation statements)

References 4 publications

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“…Consequently, a simple global measure of sharpness can be used. Such a measure was described by Doron Shaked and Ingeborg Tastl in [7], and it proceeds as follows:…”

Section: Determining the Sharpness Of An Imagementioning

confidence: 99%

Constructing Face Image Logs that are Both Complete and Concise

Fourney

Laganière

2007

Fourth Canadian Conference on Computer and Robot Vision (CRV '07)

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This paper describes a construct that we call a face image log. Face image logs are collections of time stamped images representing faces detected in surveillance videos. The techniques demonstrated in this paper strive to construct face image logs that are complete and concise in the sense that the logs contain only the best images available for each individual observed. We begin by describing how to assess and compare the quality of face images. We then illustrate a robust method for selecting high quality images. This selection process takes into consideration the limitations inherent in existing face detection and person tracking techniques. Experimental results demonstrate that face logs constructed in this manner generally contain fewer than 5% of all detected faces, yet these faces are of high quality, and they represent all individuals detected in the video sequence.

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“…Consequently, a simple global measure of sharpness can be used. Such a measure was described by Doron Shaked and Ingeborg Tastl in [7], and it proceeds as follows:…”

Section: Determining the Sharpness Of An Imagementioning

confidence: 99%

Constructing Face Image Logs that are Both Complete and Concise

Fourney

Laganière

2007

Fourth Canadian Conference on Computer and Robot Vision (CRV '07)

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“…Our calculation in equation (6) bases on the 'high to band pass frequency content ratio' explained in [14]. The difference is that our calculation is in the intensity space instead of the Fourier space.…”

Section: High To Low Frequency Energy Ratiomentioning

confidence: 99%

“…The first criterion calculates disparity of pixels from the first image and pixels from the second image using Birchfield's dissimilarity function [3]. The second criterion provides an energy ratio measurement (which corresponds to the sharpness) of both images overlapped with a certain offset δ adjusting the 'high to band pass frequency content ratio' from [14]. Our approach uses the intensity space instead of the Fourier space and applies the criterion to a given neighborhood instead of doing it globally.…”

Section: Related Workmentioning

confidence: 99%

Disparity Calculation: A Probabilistic Approach

Montoya¹,

Menhorn²,

Slomka³

2012

Signal Processing, Pattern Recognition and Applications / 779: Computer Graphics and Imaging

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This paper presents a passive method to calculate the disparity of stereoscopic images. A probabilistic approach is adopted, where two different criteria are used to find a probability density function (PDF) from which the final result can be calculated. The first criterion is a dissimilarity function used to compare the images pixel by pixel. The second is a "sharpness" criterion: Both images are overlapped with an offset in direction of the axis separating the two recording devices. As the objects in both images align better, edges overlap. The energy of the high frequency band increases while the energy in the low frequency band decreases. Due to differences in the perspective, this is only valid locally and therefore only applied to a given neighborhood. The results of both criteria are merged to a final PDF providing the final disparity. The main advantage of our approach is the parallelization ability, and thus a scalable implementation, achieving a compromise between accuracy and ease of realization. Furthermore, with minor adjustments our approach can also be used for software refocusing.

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“…Ref. [21] developed an algorithm to determine the overall SH of an image; we use their global single parameter sharpness model, implemented as the ratio between the output energy of an ideal high pass filter and an ideal band pass filter [21]:…”

Section: Multimodal Image Analysismentioning

confidence: 99%

Integrated approach to the data processing of four-dimensional datasets from phase-contrast x-ray tomography

Shahani¹,

Gulsoy²,

Gibbs³

et al. 2014

Opt. Express

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Phase contrast X-ray tomography (PCT) enables the study of systems consisting of elements with similar atomic numbers. Processing datasets acquired using PCT is nontrivial because of the low-pass characteristics of the commonly used single-image phase retrieval algorithm. In this study, we introduce an image processing methodology that simultaneously utilizes both phase and attenuation components of an image obtained at a single detector distance. This novel method, combined with regularized Perona-Malik filter and bias-corrected fuzzy C-means algorithm, allows for automated segmentation of data acquired through four-dimensional PCT. Using this integrated approach, the three-dimensional coarsening morphology of an Aluminum-29.9wt% Silicon alloy can be analyzed.

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Sharpness measure: towards automatic image enhancement

Cited by 76 publications

References 4 publications

Constructing Face Image Logs that are Both Complete and Concise

Constructing Face Image Logs that are Both Complete and Concise

Disparity Calculation: A Probabilistic Approach

Integrated approach to the data processing of four-dimensional datasets from phase-contrast x-ray tomography

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