Image Contrast Enhancement by Contourlet Transform

Nezhadarya, Ehsan; Shamsollahi, Mohammad Bagher

doi:10.1109/elmar.2006.329520

Cited by 7 publications

(9 citation statements)

References 8 publications

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“…We used the objective contrast criterion C as defined in (8) to evaluate the performance of the tested contrast enhancement methods. where max and min are respectively the maximum and minimum value of pixels in 3×3 blocks of enhanced image.…”

Section: Experimental Results and Evaluationmentioning

confidence: 99%

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A hybrid method for contrast enhancement

Najafi

Zargari

2011

2011 IEEE International Conference on Consumer Electronics -Berlin (ICCE-Berlin)

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One of the most important aspects of image quality is contrast and this paper presents a new hybrid method for contrast enhancement. The proposed method is a combination of two basic contrast enhancement methods i.e. transform and histogram. At first we apply Nonsubsampled Contourlet Transform (NSCT) on the source image, then NSCT coefficients are mapped to fuzzy domain and modified by a mapping function in fuzzy domain. After transforming the modified membership values from fuzzy domain into frequency domain, the enhanced image is reconstructed from the modified NSCT coefficients by inverse NSCT. Finally, histogram of the image is equalized by Contrast Limited Adaptive Histogram Equalization (CLAHE). The experimental results show that our method can achieve higher improvement than both Contourlet Transform (CT) andHistogram Equalization (HE) contrast enhancement methods.

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Section: Experimental Results and Evaluationmentioning

confidence: 99%

“…While [6] uses wavelet transform for contrast enhancement, [7] uses Curvelets transform and [8] uses Contourlet transform. The main idea in the most of these papers is to compute transform coefficients of the input image, modifying them by a mapping function and reconstructing the enhanced image.…”

Section: Introductionmentioning

confidence: 99%

A hybrid method for contrast enhancement

Najafi

Zargari

2011

2011 IEEE International Conference on Consumer Electronics -Berlin (ICCE-Berlin)

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“…But there still exists two problems for the contourlet-based enhancement schemes [7,8]: the first one is that some artifacts m � y be introduced, partially because the contourlet transform IS not shift-invariant, which will inevitably affect the reconstructed image after a manipulation of coefficients; another problem 978-1-4244-5540-9/10/$26.00 ©2010 IEEE 131 is that it's difficult to operate the nonlinear mapping function with many parameters.…”

Section: Introductionmentioning

confidence: 99%

Image denoising and contrast enhancement based on nonsubsampled contourlet transform

Li¹,

Chen²,

Hu³

et al. 2010

2010 3rd International Conference on Computer Science and Information Technology

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A method aimed at minimizing image noise while optimizing contrast of image subtle features based on nonsubsampled contourlet transform is presented in this paper.Nonsubsampled contourlet transform, which is a shift invariant version of the contourlet transform, has better performance in representing image edges than separable wavelet for its anisotropy, directionality and shift-invariance, and is therefore well-suited for multi-scale edge enhancement.We modify the nonsubsampled contourlet coefficients of images in corresponding subbands via a new and operable nonlinear mapping function and take the noise into account for more precise reconstruction and better visualization.Experimental results on some medical images show that the proposed enhancement method effectively highlights subtle features while suppressing noise. A comparison with other enhancement algorithms, such as histogram equalization and contourlet-based enhancement approach, is also discussed.

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“…Since conventional 2D wavelet transforms are separable, do they cannot represent non-separable structures of the image, e.g. directional curves, Nezhadarya et alii [11] proposed an innovative method to improve the sharpening of the input image using the so-called contourlet transform, whose directionality feature makes it suitable for representation of curves and edges in the image. Several solutions based on fuzzy logic have been also developed.…”

Section: Introductionmentioning

confidence: 99%

“…Several solutions based on fuzzy logic have been also developed. In [11] a fuzzy bidirectional flow framework based on generalized fuzzy sets is proposed. A fuzzy operator is also defined in [13].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sharpening with Overshoot Control

Buemi

Santoro

Bruna

et al. 2009

Image Analysis and Processing – ICIAP 2009

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Abstract. This paper presents the Adaptive Sharpening with Overshoot Control (ASOC), an algorithm for digital image contrast enhancement. The ASOC exploits a properly defined band-pass filter in order to better discriminate the "uniform/not uniform" zones of the input (blurred) image. A more aggressive sharpening is then performed on the textured zones, whilst the homogeneous regions are preserved.

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Image Contrast Enhancement by Contourlet Transform

Cited by 7 publications

References 8 publications

A hybrid method for contrast enhancement

A hybrid method for contrast enhancement

Image denoising and contrast enhancement based on nonsubsampled contourlet transform

Adaptive Sharpening with Overshoot Control

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