Anisotropic Nonstationary Image Estimation and Its Applications: Part I--Restoration of Noisy Images

Knutsson, Hans; Wilson, Roland; Granlund, Gösta H.

doi:10.1109/tcom.1983.1095832

Cited by 125 publications

(55 citation statements)

References 31 publications

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“…Detection of one-dimensionality is the aim and goal of the much older quadrature filter approach by Granlund and Knutsson [20], [29]. Here, both odd and even components of the local signal variation is detected and taken into account on the lowest level.…”

Section: Comparisons and References To Other Methodsmentioning

confidence: 99%

“…Here, both odd and even components of the local signal variation is detected and taken into account on the lowest level. Because of the quadrature, however, in the 2D-case the orientation φ of the odd squared component is limited to the same interval [ ] [20] and [29] predates the steerable filter publications [15] with approximately ten years. Interpolation is required to obtain orientation and each filter is carefully designed in the Fourier domain to get the correct angular overlap.…”

Section: Comparisons and References To Other Methodsmentioning

confidence: 99%

“…Although we use rather different tools, the general strategy is borrowed from Knutsson et al [29]. According to this strategy, local orientation features that are consistent with the more global orientation should be enhanced, the non-consistent ones suppressed.…”

Section: An Application: Fingerprint Enhancementmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Detection of Second-Degree Variations in 2D and 3D Images

Danielsson¹,

Lin²,

Ye³

2001

Journal of Visual Communication and Image Representation

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Section: Comparisons and References To Other Methodsmentioning

confidence: 99%

Section: Comparisons and References To Other Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Detection of Second-Degree Variations in 2D and 3D Images

Danielsson¹,

Lin²,

Ye³

2001

Journal of Visual Communication and Image Representation

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“…Image denoising has been a mainstay of medical image processing since its inception (Hunt, 1973;Lee, 1980;Knutsson et al, 1983). The most common algorithms include anisotropic diffusion (Perona and Malik, 1990;Weickert, 1998), bilateral filtering (Tomasi and Manduchi, 1998;Elad, 2002), adaptive filtering (Knutsson et al, 1983;Granlund and Knutsson, 1995;Westin et al, 2001) and non-local means (Buades et al, 2005;Coupe et al, 2008).…”

Section: Image Denoisingmentioning

confidence: 99%

“…The most common algorithms include anisotropic diffusion (Perona and Malik, 1990;Weickert, 1998), bilateral filtering (Tomasi and Manduchi, 1998;Elad, 2002), adaptive filtering (Knutsson et al, 1983;Granlund and Knutsson, 1995;Westin et al, 2001) and non-local means (Buades et al, 2005;Coupe et al, 2008). Besides improving overall image quality, a frequent application of image denoising in medical imaging is to counteract an increased noise level caused by lowering the amount of ionizing radiation in CT.…”

Section: Image Denoisingmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

349

198

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Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

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Directional image filter respectively adjusted to edge and flat regions

1999

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The authors propose a directional filter for eliminating noise from MRI images. Kido et al. developed a directional filter capable of detecting the minimal change direction in each image point, and of performing one‐dimensional (1D) nonlinear smoothing limited by that minimal change direction. This filter was effective in application to MRI images. In the current study, the authors improve this directional filter and develop an image filter for directional nonlinear smoothing that separates processing of edge and flat regions in an image. For the edge regions, this filter focuses on edge structure obscured by noise. It involves obtaining the group minimal change direction, taking into account the image structure in the vicinity of the current image element, and performing 1D nonlinear smoothing. This processing allows smoothing that produces neatly aligned edge directions for the adjoining image elements, even in sections where the image edge structure is obscured by noise. In addition, for the flat regions, the smoothing effect is enhanced by performing 1D nonlinear smoothing over a distance longer than that for the edge regions. Application to MRI images produces good images with neatly aligned edge directions, and enhanced smoothing effect in flat regions where excessive smoothing of the edge regions has been suppressed. ©1999 Scripta Technica, Syst Comp Jpn, 30(8): 72–78, 1999

show abstract

Anisotropic Nonstationary Image Estimation and Its Applications: Part I--Restoration of Noisy Images

Cited by 125 publications

References 31 publications

Efficient Detection of Second-Degree Variations in 2D and 3D Images

Efficient Detection of Second-Degree Variations in 2D and 3D Images

Medical image processing on the GPU – Past, present and future

Directional image filter respectively adjusted to edge and flat regions

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