• Journal of Broadcast Engineering
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• v.12 no.4
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• pp.350-359
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• 2007

In this paper we present a simple, efficient method for detection of sharp digital images. Recently many digital cameras are equipped with various autofocusing functions to help users take well-focused pictures as easily as possible. However, acquired digital pictures can be further degraded by motion, limited contrast, and inappropriate amount of exposure, to name a few. In order to decide whether to process the image or not, or whether to delete it or not, reliable measure of image quality to detect sharp images from blurry ones is needed. This paper presents a blurriness/sharpness measure, and demonstrates its feasibility using extensive experiments. This method is fast and easy to implement, and accurate. Regardless of the detection accuracy, existing measures are computation-intensive. However, the proposed measure in this paper is not demanding in computation time. Needless to say, this measure can be used for various imaging applications including autofocusing and astigmatism correction.

• Journal of Ubiquitous Convergence Technology
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• v.2 no.1
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• pp.27-39
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• 2008

In this paper we present a simple, efficient method for detecting the blurry photographs. Recently many digital cameras are equipped with various auto-focusing functions to help users take well-focused pictures as easily as possible. In addition, motion compensation devices are able to compensate motion causing blurriness in the images. However, digital pictures can be degraded by limited contrast, inappropriate exposure, imperfection of auto-focusing or motion compensating devices, unskillfulness of the photographers, and so on. In order to decide whether to process the images or not, or whether to delete them or not, reliable measure of image degradation to detect blurry images from sharp ones is needed. This paper presents a blurriness/sharpness measure, and demonstrates its feasibility by using extensive experiments. This method is fast, easy to implement and accurate. Regardless of the detection accuracy, the proposed measure in this paper is not demanding in computation time. Needless to say, this measure can be used for various imaging applications including auto-focusing and astigmatism correction.

• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.7-11
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• 2020

Enormous methods have been proposed for the detection and segmentation of blur and non-blur regions of the images. Due to the limited available information about the blur type, scenario and the level of blurriness, detection and segmentation is a challenging task. Hence, the performance of the blur measure operators is an essential factor and needs improvement to attain perfection. In this paper, we propose an effective blur measure based on the local binary pattern (LBP) with the adaptive threshold for blur detection. The sharpness metric developed based on LBP uses a fixed threshold irrespective of the blur type and level which may not be suitable for images with large variations in imaging conditions and blur type and level. Contradictory, the proposed measure uses an adaptive threshold for each image based on the image and the blur properties to generate an improved sharpness metric. The adaptive threshold is computed based on the model learned through the support vector machine (SVM). The performance of the proposed method is evaluated using a well-known dataset and compared with five state-of-the-art methods. The comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all the methods.