Image Sharpness Measure for Blurred Images in Frequency Domain

De, Kajal; Masilamani, V.

doi:10.1016/j.proeng.2013.09.086

Cited by 120 publications

(57 citation statements)

References 13 publications

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“…Following the definition of De and Masilamani [17], the sharpness in the image is defined in the frequency domain of the image as the ratio FM according to…”

Section: B Image Quality Assessmentmentioning

confidence: 99%

Comparison of image reconstruction techniques for optical projection tomography

et al. 2018

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We present a comparison of image reconstruction techniques for optical projection tomography. We compare conventional filtered back projection, sinogram filtering using the frequency-distance relationship (FDR), image deconvolution, and 2D point-spread-function-based iterative reconstruction. The latter three methods aim to remove the spatial blurring in the reconstructed image originating from the limited depth of field caused by the point spread function of the imaging system. The methods are compared based on simulated data, experimental optical projection tomography data of single fluorescent beads, and high-resolution optical projection tomography imaging of an entire zebrafish larva. We demonstrate that the FDR method performs poorly on data acquired with high numerical aperture optical imaging systems. We show that the deconvolution technique performs best on highly sparse data with low signal-to-noise ratio. The point-spread-function-based reconstruction method is superior for nonsparse objects and data of high signal-to-noise ratio.

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“…Following the definition of De and Masilamani [17], the sharpness in the image is defined in the frequency domain of the image as the ratio FM according to…”

Section: B Image Quality Assessmentmentioning

confidence: 99%

Comparison of image reconstruction techniques for optical projection tomography

et al. 2018

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“…(2) From graph (1), we can determine the best enhancement for set of image using alpha -trimmed mean filter, which have less number of errors.…”

Section: Results and Analysismentioning

confidence: 99%

“…In addition to standard measurement, tools we also focus on create math model to measure how much image was been blurred after processing it [2]. Blur=Max (difference between two vertical variations of two image (input and blurred), difference between two horizontal variations of two image (input and blurred))…”

Section: Graph (1) Errors Between Processed Images Based Different Tymentioning

confidence: 99%

Statistics and Mean Filters Based on Spatial Noise: A Comparative Study

Habib¹

2016

International Journal of Advanced Research in Computer and Comm

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Abstract:The main idea of this paper to exhibit study which illuminate set of characteristics among statistics and mean filters rely on additive and multiplicative in addition salt and pepper noise with compare among them using difference type analysis methods and blur metric . We selected one filter form statistics filters and another one from means filters. Finally, we get perfect results tell as what is the best filter and affect them on noises [3].

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“…We follow a simple sharpness metric proposed by Kanjar De et al [30]. Given an image I of size h × w, we first compute its Fourier transform representation F .…”

Section: Evaluation Of Original Training Setmentioning

confidence: 99%

Cross-Resolution Person Re-identification with Deep Antithetical Learning

Zhuang

Chen

et al. 2019

Computer Vision – ACCV 2018

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Images with different resolutions are ubiquitous in public person re-identification (ReID) datasets and real-world scenes, it is thus crucial for a person ReID model to handle the image resolution variations for improving its generalization ability. However, most existing person ReID methods pay little attention to this resolution discrepancy problem. One paradigm to deal with this problem is to use some complicated methods for mapping all images into an artificial image space, which however will disrupt the natural image distribution and requires heavy image preprocessing. In this paper, we analyze the deficiencies of several widely-used objective functions handling image resolution discrepancies and propose a new framework called deep antithetical learning that directly learns from the natural image space rather than creating an arbitrary one. We first quantify and categorize original training images according to their resolutions. Then we create an antithetical training set and make sure that original training images have counterparts with antithetical resolutions in this new set. At last, a novel Contrastive Center Loss(CCL) is proposed to learn from images with different resolutions without being interfered by their resolution discrepancies. Extensive experimental analyses and evaluations indicate that the proposed framework, even using a vanilla deep ReID network, exhibits remarkable performance improvements. Without bells and whistles, our approach outperforms previous state-of-the-art methods by a large margin.

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Image Sharpness Measure for Blurred Images in Frequency Domain

Cited by 120 publications

References 13 publications

Comparison of image reconstruction techniques for optical projection tomography

Comparison of image reconstruction techniques for optical projection tomography

Statistics and Mean Filters Based on Spatial Noise: A Comparative Study

Cross-Resolution Person Re-identification with Deep Antithetical Learning

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