A content-adaptive sharpness enhancement algorithm using 2D FIR filters trained by pre-emphasis

Choi, Ik Hyun; Nam, Yeon-Oh; Song, Byung Cheol

doi:10.1016/j.jvcir.2013.04.003

Cited by 1 publication

(2 citation statements)

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“…We used subjective and objective aspects to evaluate the image enhancement effects. In terms of the objective metrics, six evaluation metrics were applied, namely information entropy (H) [32], mean square error (MSE) [33], peak signal-to-noise ratio (PSNR) [34], gradient magnitude similarity deviation (GMSD) [35], mean absolute error (MAE) [36], and multi-scale structural similarity (MSSSIM) [37]. For the indicators of H, PSNR, and MSSSIM, larger values demonstrate a better enhancement of the image; for the other three metrics MSE, GMSD, and MAE, lower values demonstrate a better enhancement of the image.…”

Section: Resultsmentioning

confidence: 99%

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Remote Sensing Image Enhancement Based on Non-Local Means Filter in NSCT Domain

Jia

2017

Algorithms

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Abstract:In this paper, a novel remote sensing image enhancement technique based on a non-local means filter in a nonsubsampled contourlet transform (NSCT) domain is proposed. The overall flow of the approach can be divided into the following steps: Firstly, the image is decomposed into one low-frequency sub-band and several high-frequency sub-bands with NSCT. Secondly, contrast stretching is adopted to deal with the low-frequency sub-band coefficients, and the non-local means filter is applied to suppress the noise contained in the first high-frequency sub-band coefficients. Thirdly, the processed coefficients are reconstructed with the inverse NSCT transform. Finally, the unsharp filter is used to enhance the details of the image. The simulation results show that the proposed algorithm has better performance in remote sensing image enhancement than the existing approaches.

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Section: Resultsmentioning

confidence: 99%

“…where g x,y and f x,y denote the original image pixel and the enhanced image pixel, respectively [36]. If the MAE is lower, then the image quality is better.…”

Section: Objective Analysismentioning

confidence: 99%

Remote Sensing Image Enhancement Based on Non-Local Means Filter in NSCT Domain

Jia

2017

Algorithms

View full text Add to dashboard Cite

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A content-adaptive sharpness enhancement algorithm using 2D FIR filters trained by pre-emphasis

Cited by 1 publication

References 22 publications

Remote Sensing Image Enhancement Based on Non-Local Means Filter in NSCT Domain

Remote Sensing Image Enhancement Based on Non-Local Means Filter in NSCT Domain

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