Denoising-Enhancing Images on Elastic Manifolds

Ratner, V. A.; Zeevi, Yehoshua Y.

doi:10.1109/tip.2011.2118221

Cited by 18 publications

(7 citation statements)

References 29 publications

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“…Compared with the traditional P-M model, the new model effectively extracted image edge features. Ratner used the telegram diffusion equation [14] to substantially reserve the details; hence, it is extensively applied in image processing. Accordingly, You and Kaveh proposed a fourth-order partial differential equation [15] and Bai and Feng introduced a fractional-order partial differential equation [16], thereby enabling the further development of the partial differential equation obtained in the aspect of image denoising.…”

Section: State Of the Artmentioning

confidence: 99%

Self-Adaptive Anisotropic Image Enhancement Algorithm Based on Local Variance

Chen¹,

Bai²,

Zhang³

et al. 2017

JESTR

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When an image is enhanced using the traditional Laplacian enhancement model, the enhancement effect is evident but the overshoot phenomenon occurs simultaneously as the neighborhood and weight increase. To solve the contradiction between edge preservation and noise suppression during the image enhancement process, this study proposed an improved partial differential equation image enhancement algorithm. The algorithm combined the neighborhood features of digital images, mined the relationship between the local variance and the detail information of images, and classified the image noise and detail information with local variance. On this basis, the periodic change features of the trigonometric function were used to construct anisotropic diffusion coefficients, the numerical solution method of the partial differential equation was used to calculate the numerical solution of the proposed algorithm, and simulation experiment was implemented to analyze the influence of the algorithm model parameters on enhancement effect. Lastly, the anisotropic enhancement algorithm proposed in this study was validated through comparison with the traditional Laplace algorithm. Results indicate that tangential direction parameter and the number of iterations exert influence on the image enhancement effects. That is, when tangential direction parameter is set as 0.5 and the iterative operation is conducted 5 times, the algorithm smoothens the noise and enhances the image details. When the proposed algorithm is used to process low-contrast industrial image, the signal-to-noise ratio of the enhanced image is increased by 8 db and the mean squared error is reduced by 55% compared with the enhancement effect of the traditional Laplace enhancement method. In terms of subjective visual effect, the noise of the enhanced image is filtered, whereas detailed information (e.g., numerous weak edges) is reserved. Hence, subjective and objective evaluations reflected that the proposed algorithm can effectively enhance image details and avoid over-enhancement. Moreover, the proposed algorithm is of favorable adaptability and certain reference value to low-contrast image enhancement.

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Section: State Of the Artmentioning

confidence: 99%

Self-Adaptive Anisotropic Image Enhancement Algorithm Based on Local Variance

Chen¹,

Bai²,

Zhang³

et al. 2017

JESTR

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“…The most popular is certainly the heat or diffusion equation, but other non-linear second-order [16], fourthorder [12] and, very recently, fractional-order PDEs [3] have been deployed for image processing. Although the great majority of these equations are parabolic, researchers are also investigating on the use of hyperbolic equations, such as the shock-filters [15] or the telegrapher equation [17].…”

Section: Introductionmentioning

confidence: 99%

Keypoints from symmetries by wave propagation

Salti

Lanza

Stefano

2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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The paper conjectures and demonstrates that repeatable keypoints based on salient symmetries at different scales can be detected by a novel analysis grounded on the wave equation rather than the heat equation underlying traditional Gaussian scale-space theory. While the image structures found by most state-of-the-art detectors, such as blobs and corners, occur typically on planar highly textured surfaces, salient symmetries are widespread in diverse kinds of images, including those related to untextured objects, which are hardly dealt with by current feature-based recognition pipelines. We provide experimental results on standard datasets and also contribute with a new dataset focused on untextured objects. Based on the positive experimental results, we hope to foster further research on the promising topic of scale invariant analysis through the wave equation.

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“…Ratner further advance this algorithm and an adaptive lowpass filter is proposed which is better than the adaptive diffusion filter to retain the high frequency components. 5 The authors study the image deconvolution from a new perspective, establishment of a filter group, which is a generalized image deblurring model. 6 Very often, degraded image caused by defocus, defocus can be seen as a diffusion process, the heat conduction equation can describe the blurred images corresponding to thermal diffusion.…”

Section: Introductionmentioning

confidence: 99%

Generalized TV and Sparse Decomposition of the Ultrasound Image Deconvolution Model Based on Fusion Technology

Wen

Wan²

2013

j. nanosci. nanotech.

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Ultrasound image deconvolution involves noise reduction and image feature enhancement, denoising need equivalent the low-pass filtering, image feature enhancement is to strengthen the high-frequency parts, these two requirements are often combined together. It is a contradictory requirement that we must be reasonable balance between these two basic requirements. Image deconvolution method of partial differential equation model is the method based on diffusion theory, and sparse decomposition deconvolution is image representation-based method. The mechanisms of these two methods are not the same, effect of these two methods own characteristics. In contourlet transform domain, we combine the strengths of the two deconvolution method together by image fusion, and introduce the entropy of local orientation energy ratio into fusion decision-making, make a different treatment according to the actual situation on the low-frequency part of the coefficients and the high-frequency part of the coefficient. As deconvolution process is inevitably blurred image edge information, we fusion the edge gray-scale image information to the deconvolution results in order to compensate the missing edge information. Experiments show that our method is better than the effect separate of using deconvolution method, and restore part of the image edge information.

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Denoising-Enhancing Images on Elastic Manifolds

Cited by 18 publications

References 29 publications

Self-Adaptive Anisotropic Image Enhancement Algorithm Based on Local Variance

Self-Adaptive Anisotropic Image Enhancement Algorithm Based on Local Variance

Keypoints from symmetries by wave propagation

Generalized TV and Sparse Decomposition of the Ultrasound Image Deconvolution Model Based on Fusion Technology

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