No-reference multi-scale blur metric

Harrity, Kyle; Ezekiel, Soundararajan; Ferris, Michael H.; Cornacchia, Maria; Blasch, Erik

doi:10.1109/naecon.2014.7045786

Cited by 6 publications

(6 citation statements)

References 26 publications

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“…A small threshold will produce a sharper signal but there is a higher chance that it will be noisy. A larger threshold will produce a smoother signal but there is a higher chance that it will be blurry [7]. The thresholding of wavelet coefficients is usually only applied to the detail coefficients rather than the approximation coefficients.…”

Section: G Thresholdingmentioning

confidence: 99%

“…c 1 is defined below ( 7 ) where L is the dynamic range of the signal values (0 to Max ) and K 1 is a small consant less than one. The constrat comparison function takes a similar form to the luminance function.…”

Section: Structural Similarity Indexmentioning

confidence: 99%

“…Curvelets and contourlets have been routinely used to denoise [5,6] and deblur [7] images. In this paper we seek metrics for comparative results [8] with different noise types [9] to assess denoising one-dimensional signals with the curvelets and contourlets transformations.…”

Section: Introductionmentioning

confidence: 99%

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Denoising one-dimensional signals with curvelets and contourlets

Moore

Ezekiel

Blasch

2014

NAECON 2014 - IEEE National Aerospace and Electronics Conference

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Fast Fourier Transforms (FFTs) and Discrete Wavelet Transformations (DWTs) have been routinely used as methods of denoising signals. DWT limitations include the inability to detect contours, curves and directional information of multi-dimensional signals. In the past decade, two new approaches have surfaced: curvelets, developed by Candès; and contourlets, developed by Do et al. The typical applications of contourlets and curvelets include twodimensional image data denoising. We explore the use of curvelets and contourlets to the one-dimensional (1D) denoising problem. Working with seismic data, we introduce various types of data noise and the wavelet, curvelet, and contourlet transforms are applied to each signal. We tested multiple decomposition levels and different thresholding values. The benchmark for determining the effectiveness of each transform is the peak signal-to-noise ratio (PSNR) between the original signal and the denoised signal. The proposed denoising methods demonstrate contourlets and curvelets as a viable alternative to the DWT and FFT during signal processing. The initial results indicate that the contourlet and curvelet methods yield a higher PSNR and lower error than the DWT and FFT for 1D data.

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Section: G Thresholdingmentioning

confidence: 99%

Section: Structural Similarity Indexmentioning

confidence: 99%

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Denoising one-dimensional signals with curvelets and contourlets

Moore

Ezekiel

Blasch

2014

NAECON 2014 - IEEE National Aerospace and Electronics Conference

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“…This research has resulted in many alternative transforms such as the Ridgelet transform [31], Contourlet transform [32], double-density DWT [33,34], dual-tree DWT [35,36,37], and the combined D3TDWT [38]. Some of these such as the Contourlet transform have shown promise for detecting edge width as we intend to do in this work [39]. For this paeper, we focus on the useful traits of the D3TDWT for isolation of edge widths in an image.…”

Section: Introductionmentioning

confidence: 99%

“…A graphical representation of the D3TDWT metric. Many approaches have been tried to determine the blur content that we have been investigating for various applications including the gradient DWT transform [40], coutourlet [41], and curvelet. For the experiment, we were interested in the promise of the D3TDWT for blur analysis.…”

Section: Introductionmentioning

confidence: 99%

No-reference blur metric using double-density and dual-tree two-dimensional wavelet transformation

Ezekiel

Harrity

Blasch

et al. 2014

NAECON 2014 - IEEE National Aerospace and Electronics Conference

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Over the past decade the digital camera has become widely available in many devices such as cell phones, computers, etc. Therefore, the perceptual quality of digital images is an important and necessary requirement to evaluate digital images. To improve the quality of images captured with camera, we must identify and measure the artifacts that cause blur within the images. Blur is mainly caused by pixel intensity due to multiple sources. The most common types of blurs are known as object motion, defocus, and camera motion. In the last two decades, the discrete wavelet transformation (DWT) has become a cutting-edge technology in the signal and image processing field for such applications as denoising. The disadvantage of the DWT is that it is not able to directly observe blur coefficients. In this paper, we propose a novel framework for a blur metric for an image. Our approach is based on the double-density dual tree two dimensional wavelet transformations (D3TDWT) which simultaneously processes the properties of both the double-density DWT and dual tree DWT. We also utilize gradient to evaluate blurring artifacts and measure the image quality.

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