Matlab noise reduction based on partial-reference, dual-tree complex wavelet transform shrinkage

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NOISE REDUCTION BASED ON PARTIAL-REFERENCE, DUAL-TREE COMPLEX
WAVELET TRANSFORM SHRINKAGE
ABSTRACT:
This paper presents a novel way to reduce noise introduced or exacerbated by image
enhancement methods, in particular algorithms based on the random spray sampling technique,
but not only. According to the nature of sprays, output images of spray-based methods tend to
exhibit noise with unknown statistical distribution. To avoid inappropriate assumptions on the
statistical characteristics of noise, a different one is made. In fact, the non-enhanced image is
considered to be either free of noise or affected by non-perceivable levels of noise. Taking
advantage of the higher sensitivity of the human visual system to changes in brightness, the
analysis can be limited to the luma channel of both the non-enhanced and enhanced image. Also,
given the importance of directional content in human vision, the analysis is performed through
the dual-tree complex wavelet transform (DTWCT).
The discrete wavelet transform, the DTWCT allows for distinction of data directionality in the
transform space. For each level of the transform, the standard deviation of the non-enhanced
image coefficients is computed across the six orientations of the DTWCT, then it is normalized.
The result is a map of the directional structures present in the non-enhanced image. Said map is
then used to shrink the coefficients of the enhanced image. The shrunk coefficients and the
coefficients from the non-enhanced image are then mixed according to data directionality.
Finally, a noise-reduced version of the enhanced image is computed via the inverse transforms.
A thorough numerical analysis of the results has been performed in order to confirm the validity
of the proposed approach.
ECWAY TECHNOLOGIES
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VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com

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Matlab noise reduction based on partial-reference, dual-tree complex wavelet...

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Ecway Technologies provides IEEE projects and software development services from its offices in multiple cities across India. The company specializes in noise reduction techniques based on partial-reference, dual-tree complex wavelet transform shrinkage. This novel approach reduces noise introduced by image enhancement methods like random spray sampling. It analyzes the luma channel of the original and enhanced images using dual-tree complex wavelet transform to distinguish directional data. Coefficients from the enhanced image are shrunk based on the standard deviation of coefficients from the original image. The shrunk and original coefficients are then mixed according to directionality to generate a noise-reduced version of the enhanced image. Testing validated the effectiveness of this partial-reference, dual-tree complex

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This paper proposes a novel method for reducing noise introduced by image enhancement methods such as random spray sampling. The method uses the dual-tree complex wavelet transform to analyze the luma channel of the original non-enhanced image and enhanced noisy image. It computes the standard deviation of coefficients across orientations in the original image to create a directional structure map. This map is used to shrink the coefficients of the enhanced image before mixing with the original image coefficients based on orientation. This allows noise reduction without assumptions about the noise statistics, using the original image as a partial reference. Evaluation shows the validity of the approach.

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Matlab noise reduction based on partial-reference, dual-tree complex wavelet transform shrinkage

1. NOISE REDUCTION BASED ON PARTIAL-REFERENCE, DUAL-TREE COMPLEX WAVELET TRANSFORM SHRINKAGE ABSTRACT: This paper presents a novel way to reduce noise introduced or exacerbated by image enhancement methods, in particular algorithms based on the random spray sampling technique, but not only. According to the nature of sprays, output images of spray-based methods tend to exhibit noise with unknown statistical distribution. To avoid inappropriate assumptions on the statistical characteristics of noise, a different one is made. In fact, the non-enhanced image is considered to be either free of noise or affected by non-perceivable levels of noise. Taking advantage of the higher sensitivity of the human visual system to changes in brightness, the analysis can be limited to the luma channel of both the non-enhanced and enhanced image. Also, given the importance of directional content in human vision, the analysis is performed through the dual-tree complex wavelet transform (DTWCT). The discrete wavelet transform, the DTWCT allows for distinction of data directionality in the transform space. For each level of the transform, the standard deviation of the non-enhanced image coefficients is computed across the six orientations of the DTWCT, then it is normalized. The result is a map of the directional structures present in the non-enhanced image. Said map is then used to shrink the coefficients of the enhanced image. The shrunk coefficients and the coefficients from the non-enhanced image are then mixed according to data directionality. Finally, a noise-reduced version of the enhanced image is computed via the inverse transforms. A thorough numerical analysis of the results has been performed in order to confirm the validity of the proposed approach. ECWAY TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE BANGALORE / HYDRABAD CELL: +91 98949 17187, +91 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 / 8111 VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com

Matlab noise reduction based on partial-reference, dual-tree complex wavelet transform shrinkage

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