• Journal of Broadcast Engineering
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• v.16 no.3
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• pp.552-560
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• 2011

In this paper, a new no-reference sharpness measure using frequency domain coefficients is proposed. Although most existing sharpness measures used pixel intensity to compute the blur degree, the proposed sharpness measure computes the sharpness using frequency coefficients. To assess the perceived sharpness of a given image, the image is re-blurred by a Gaussian low pass filter and a new quality measure function was defined using the frequency domain coefficients of the given image and the re-blurred image. To evaluate the proposed algorithms, TID2008 quality assessment database was used. Experimental results show that the proposed quality assessment method showed high correlation with the subjective scores.

• Smart Structures and Systems
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• v.24 no.5
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• pp.659-668
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• 2019

In digital image correlation, speckle image is closely related to the measurement accuracy. A practical global evaluation criterion for speckle image is presented. Firstly, based on the essential factors of the texture image, both the average particle size and image sharpness are used for the assessment of speckle image. The former is calculated by a simplified auto-covariance function and Gaussian fitting, and the latter by focusing function. Secondly, the computation of the average particle size and image sharpness is verified by numerical simulation. The influence of these two evaluation parameters on mean deviation and standard deviation is discussed. Then, a physical model from speckle projection to image acquisition is established. The two evaluation parameters can be mapped to the physical devices, which demonstrate that the proposed evaluation method is reasonable. Finally, the engineering application of the evaluation method is pointed out.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2529-2543
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• 2019

Scanning electron microscopy (SEM) image can link with the microscopic world through reflecting interaction between electrons and materials. The SEM images are easily subject to blurring distortions during the imaging process. Inspired by the fact that dark channel prior captures the changes to blurred SEM images caused by the blur process, we propose a method to evaluate the SEM images sharpness based on the dark channel prior. A SEM image database is first established with mean opinion score collected as ground truth. For the quality assessment of the SEM image, the dark channel map is generated. Since blurring is typically characterized by the spread of edge, edge of dark channel map is extracted. Then noise is removed by an edge-preserving filter. Finally, the maximum gradient and the average gradient of image are combined to generate the final sharpness score. The experimental results on the SEM blurred image database show that the proposed algorithm outperforms both the existing state-of-the-art image sharpness metrics and the general-purpose no-reference quality metrics.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.275-278
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• 2002

Sharpness enhancement, which strengthen the edge(high frequency) of image, is widely studied for image processing research area. In this paper, psychophysical experiment is conducted by the 20 observers with simple linear unsharp masking for sharpness enhancement. The experimental results extracted using z-score analysis and linear regression suggests observer preferable sharpness enhancement method for digital television.

• Current Optics and Photonics
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• v.5 no.5
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• pp.514-523
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• 2021

Methods for absolute depth estimation have received lots of interest, and most algorithms are concerned about how to minimize the difference between an input defocused image and an estimated defocused image. These approaches may increase the complexity of the algorithms to calculate the defocused image from the estimation of the focused image. In this paper, we present a new method to recover depth of scene based on a sharpness-assessment algorithm. The proposed algorithm estimates the depth of scene by calculating the sharpness of deconvolved images with a specific point-spread function (PSF). While most depth estimation studies evaluate depth of the scene only behind a focal plane, the proposed method evaluates a broad depth range both nearer and farther than the focal plane. This is accomplished using an asymmetric aperture, so the PSF at a position nearer than the focal plane is different from that at a position farther than the focal plane. From the image taken with a focal plane of 160 cm, the depth of object over the broad range from 60 to 350 cm is estimated at 10 cm resolution. With an asymmetric aperture, we demonstrate the feasibility of the sharpness-assessment algorithm to recover absolute depth of scene from a single defocused image.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.32-39
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• 2012

This paper develops a robust image enhancement method by adjusting image signal energy according to the direction and the variation of image signal in DCT domain. To accomplish this, we measure the gradient of image signal directly in DCT domain and then adjust frequency components involved in sharpness, local contrast and global contrast using the direction and the magnitude of the measured gradient The experiment showed that the proposed method produces the best quality of an image without causing blocking, ringing artifacts and boosting noise.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08b
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• pp.1692-1695
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• 2007

We describe the image enhancement method of applying two spatial filters with different characteristics adaptively. An adaptive method is introduced so that sharpness enhancement is performed only in regions where the image exhibits significant dynamics, while noise reduction is achieved in smooth regions. Simulation results show that the proposed method improved the image quality.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.127-136
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• 2013

This paper proposes a video deblurring algorithm which maintains uniform sharpness between frames. Unlike the previous algorithms using fixed parameters, the proposed algorithm keeps uniform sharpness by adjusting parameters for each frame. First, we estimate the initial blur kernel and perform deconvolution, then measure the sharpness of the deblurred image. In order to maintain uniform sharpness, we adjust the regularization parameter and kernel according to the examined sharpness, and perform deconvolution again. The experimental results show that the proposed algorithm achieves outstanding deblurring results while providing consistent sharpness.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.905-923
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• 2018

In order to generate high-resolution products using UAV images, it is necessary to analyze the sharpness of the themselves measured through image analysis. When images that have unclear sharpness of UAV are used in the production, they can have a great influence on operations such as acquisition and mapping of accurate three-dimensional information using UAV. GRD (Ground Resolved Distance) has been used as an indicator of image clarity. GRD is defined as the minimum distance between two identifiable objects in an image and is used as a concept against the GSD (Ground Sampling Distance), which is a spatial sample interval. In this study, GRD is extracted by analyzing the edge target without visual analysis. In particular, GRD to GSD ratio (GRD/GSD), or GRD expressed in pixels, is used as an index for evaluation the relative image sharpness. In this paper, GRD is calculated by analyzing edge targets at various altitudes in various shooting environments using a rotary wing. Using GRD/GSD, it was possible to identify images whose sharpness was significantly lowered, and the appropriateness of the image as an image clarity index was confirmed.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.2
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• pp.70-77
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• 2010

This study proposed an auto focusing method for a multi-focus image in assembling lens modules in digital camera phones. A camera module in a camera phone is composed of a lens barrel, an IR glass, a lens mount, a PCB board and aspheric lenses. Alignment among the components is one of the important factors in product quality. Auto-focus is essential to adjust image quality of an IR glass in a lens holder, but there are two focal points in the captured image due to thickness of IR glass. So, sharpness, probability and a scale factor are defined to find desired focus from a multi-focus image. The sharpness is defined as clarity of an image. Probability and a scale factors are calculated using pattern matching with a registered image. The presented algorithm was applied to a lens assembly machine which has 5 axes, two vacuum chucks and an inspection system. The desired focus can be determined on the local maximum of the sharpness, the probability and the scale factor in the experiment.