Bandelet denoising in image processing

McLaughlin, Michael J.; Grieggs, Samuel; Ezekiel, Soundararajan; Ferris, Michael H.; Blasch, Erik; Alford, Mark; Cornacchia, Maria; Bubalo, Adnan

doi:10.1109/naecon.2015.7443035

Cited by 3 publications

(3 citation statements)

References 41 publications

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“…The bandelet transform is simpler and useful for denoising and deblurring due to its above properties, because of the geometric structures carry most of the perceptual information in the medical image. 37 The properties of bandelet are presented as follows [32][33][34]37 :…”

Section: Increasing the Quality Of Medical Imagesmentioning

confidence: 99%

“…The time for processing is long. The bandelet transform is simpler and useful for denoising and deblurring due to its above properties, because of the geometric structures carry most of the perceptual information in the medical image 37 . The properties of bandelet are presented as follows 32–34,37 : (i)Geometric flow direction: to find the optimal representation of linear structures. (ii)Geometric regularity: to compute polynomial approximations in localized regions. …”

Section: Object Contour In Low‐quality Medical Imagesmentioning

confidence: 99%

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Object contour based on improving region growing method and context border condition

Binh

Tuyet

Thành

2019

Concurrency and Computation

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Image diagnosis is a very significant medical specialty. Thereby, the doctor will see the form and function of the structure inside the body. The recorded images will be an important basis to help doctors find the disease and treatment more effectively. However, the quality of medical images is a vital factor. Improving the quality of medical images and object contour are the major challenges, because each tiny detail of medical images can contain much information. Object contour in good medical image quality is hard work and it is more difficult in low-quality medical image. The organs in the image are not clear, which is difficult for the doctor to distinguish the border of the organs in the image. In this paper, we proposed a method for object contour based on improving region growing in bandelet domain in low-quality medical images. The method includes two stages: increasing the quality of medical image in bandelet domain and object contour by improving region growing combined with context border condition in medical images in the first stage. The result of the proposed method is better than the other methods.

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Section: Increasing the Quality Of Medical Imagesmentioning

confidence: 99%

Section: Object Contour In Low‐quality Medical Imagesmentioning

confidence: 99%

Object contour based on improving region growing method and context border condition

Binh

Tuyet

Thành

2019

Concurrency and Computation

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“…These methods are theoretically clear, but their framework is complex for estimation and the occurrence of incomplete deconvolution may result in an inefficient model. Multiscale and multiresolution methods extract high-frequency parts from HGRI and inject them into HSRI by decomposing the source image in a specific domain [17] [18] [19] [20] [21]. However, this type of method is highly dependent on the spectral properties of sampling methods and fails to intensify the spatial resolution of all bands.…”

Section: State Of the Artmentioning

confidence: 99%

Image Fusion in Remote Sensing Based on Spectral Unmixing and Improved Non - negative Matrix Factorization Algorithm

He¹,

Wang²,

Lai³

et al. 2018

JESTR

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The spectral resolution of images improves with the continuous development of imaging technology in remote sensing. Traditional image fusion technologies result in problems in hyperspectral image fusion, such as spectral distortion and unclear physical meaning. A fast model based on improved non-negative matrix factorization (INMF) for hyperspectral and panchromatic image fusion was proposed to optimize the results of image fusion. First, the hyperspectral image was decomposed into endmember and abundance matrices using the INMF algorithm. The panchromatic image was then used to sharpen the abundance matrix. The spectral and sparse constraints were imposed on the objective function of the model. Finally, the resultant fused image was obtained by reconstructing the resolved endmember and abundance matrices. Results show that the proposed method is superior to other methods in terms of subjective assessment and objective analysis. As far as the indices of information entropy, correlation coefficient, Q-average, and spectral divergence are concerned, the proposed method surpasses those of second-best methods (Ehler, classical NMF, Ehler and Ehler) by 2.06%, 0.36%, 0.91%, and 56.31%, respectively, in the synthetic data experiment, and exceeds those of the second-best methods (Ehler, high-pass filtering, high-pass filtering and Ehler) by 0.13%, 10.05%, 3.89%, and 7.26%, respectively, in the real data experiment. Moreover, runtime is proportional to data size, and the proposed method takes the least time when image size is between 1 MB and 480 MB. This study provides theoretical reference for fast hyperspectral and panchromatic image fusion.

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Contourlet image preprocessing for enhanced control point selection in airborne image registration

Sobolewski

Messer

Lutz

et al. 2015

2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

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Bandelet denoising in image processing

Cited by 3 publications

References 41 publications

Object contour based on improving region growing method and context border condition

Object contour based on improving region growing method and context border condition

Image Fusion in Remote Sensing Based on Spectral Unmixing and Improved Non - negative Matrix Factorization Algorithm

Contourlet image preprocessing for enhanced control point selection in airborne image registration

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