A New Sharpness Measure Based on Gaussian Lines and Edges

Dijk, J. C. van; Ginkel, M. Van; Asselt, Rutger J. van; Vliet, Lucas J. van; Verbeek, P.W.

doi:10.1007/978-3-540-45179-2_19

Cited by 30 publications

(16 citation statements)

References 5 publications

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“…Since the human visual system (HVS) is the ultimate receiver of most visual signal after various processing (compression, restoration, enhancement, etc. ), there has been substantial research effort [2]- [17] to incorporate relevant perceptual characteristics for visual distortion/quality evaluation (as a stand-alone metric or a module in the abovementioned processing). Some of the perceptual evaluation techniques have been surveyed in [18], [19].…”

Section: Introductionmentioning

confidence: 99%

“…The former class needs both the original (or reference) and the processed images, and is further divided into two subclasses: reduced-reference (RR) [13] and full-reference (FR) (the MAE/MSE/PSNR related metrics and the majority of the existing perceptual metrics [2]- [12] belong to this subclass). The latter [also called no-reference (NR)] class [14]- [17] evaluated and can be therefore adopted when the reference image is not available or too expensive to transmit/process. From the viewpoint of methodology, there are two major paradigms of distortion metrics.…”

Section: Introductionmentioning

confidence: 99%

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Visual distortion gauge based on discrimination of noticeable contrast changes

Wang

Dong

Xue

2005

IEEE Trans. Circuits Syst. Video Technol.

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This paper presents a method to discriminate pixel differences according to their impact toward perceived visual quality. Noticeable local contrast changes are formulated firstly since contrast is the basic sensory feature in the human visual system (HVS) perception. The analysis aims at quantifying the actual impact of such changes (further divided into increases and decreases on edges) in different signal contexts. An associated full-reference distortion metric proposed next provides better match with the HVS viewing. Experiments have used two independent visual data sets and the related subjective viewing results, and demonstrated the performance improvement of the proposed metric over the relevant existing ones with various video/images and under diversified test conditions. The proposed metric is particularly effective to visual signal with blurring and luminance fluctuations as the major artifacts, and brings about the fundamental improvement when sharpened image edges are involved.Index Terms-Edge contrast increase, human visual system (HVS), just noticeable difference (JND), perceptual visual quality, subjective quality ratings.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Visual distortion gauge based on discrimination of noticeable contrast changes

Wang

Dong

Xue

2005

IEEE Trans. Circuits Syst. Video Technol.

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“…A filter bank, adjusted to various edge orientations, was used by Dijk et al to detect the average edge width [23]. As an improvement over the global kurtosis sharpness measure [19], Caviedes et al proposed a local kurtosis sharpness measure based on detected edges [24].…”

Section: B Sharpness Measuresmentioning

confidence: 99%

Extreme Zoom Surveillance: System Design and Image Restoration

Yao¹,

Abidi²,

Abidi³

2007

JMM

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Abstract-Applications of digital imaging with extreme zoom are traditionally found in astronomy and wild life monitoring. More recently, the need for such capabilities has extended to long range surveillance and wide area monitoring such as forest fires, harbors, and waterways. In this paper, we present a number of sensor arrangements for such applications, focusing on optical setups, auto-focusing mechanisms, and image deblurring techniques. Considering both the speed of convergence and robustness to image degradations induced by high system magnifications and long observation distances, we introduce an auto-focusing algorithm based on sequential search with a variable step size. We derive the transition criteria following maximum likelihood (ML) estimation for the selection of suitable step sizes. The efficiency of the proposed algorithm is illustrated in real-time auto-focusing and tracking of faces from distances of 50m~300m.We also develop an image restoration algorithm for high magnification imaging systems, where an adaptive sharpness measure is employed as a cost function to guide the fine search for an optimal point spread function (PSF) for image deblurring. Experimental results demonstrate a considerably enhanced robustness to image noise and artifacts and ability to select the optimum PSF, producing superior restored images.

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“…However, the difficulties in detecting and isolating step edges still remain. A filter bank, adjusted to various edge orientations, was used by Dijk et al to detect the average edge width [4].…”

Section: Edge Based Measuresmentioning

confidence: 99%

“…In addition to some well known sharpness measures, such as the Tenengrad and Laplacian measures [2], edge based sharpness measures [3][4][5], where local characteristics of strong edges are explored, have emerged in recent years. This paper looks into various sharpness measures published to date and compares their performances using high magnification images.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of sharpness measures and search algorithms for the auto focusing of high-magnification images

et al. 2006

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Digital imaging systems with extreme zoom capabilities are traditionally found in astronomy and wild life monitoring. More recently, the need for such capabilities has extended to long range surveillance and wide area monitoring such as forest fires, airport perimeters, harbors, and waterways. Auto-focusing is an indispensable function for imaging systems designed for such applications. This paper studies the feasibility of an image based passive auto-focusing control for high magnification systems based on off-the-shelf telescopes and digital cameras/camcorders, with concentration on two associated elements: the cost function (usually the image sharpness measure) and the search strategy. An extensive review of existing sharpness measures and search algorithms is conducted and their performances compared. In addition, their applicability and adaptability to a wide range of high magnifications (50×~1500×) are addressed. This study builds up the foundation for the development of auto-focusing schemes with particular applications to high magnification systems.

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A New Sharpness Measure Based on Gaussian Lines and Edges

Cited by 30 publications

References 5 publications

Visual distortion gauge based on discrimination of noticeable contrast changes

Visual distortion gauge based on discrimination of noticeable contrast changes

Extreme Zoom Surveillance: System Design and Image Restoration

Evaluation of sharpness measures and search algorithms for the auto focusing of high-magnification images

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