Noise and Signal Activity Maps for Better Imaging Algorithms

Kisilev, Pavel; Shaked, Doron; Lim, Suk Hwan

doi:10.1109/icip.2007.4379106

Cited by 8 publications

(10 citation statements)

References 5 publications

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“…Prior to performing the denoising, we first transform the RGB image into the YCbCr space and apply the noise estimator [8] to estimate the noise STD profile directly from the image. Three separate image pyramids are generated for Y, Cb and Cr channels and the methods described in Section 2.2 and 2.3 are applied in the Multi-resolution (MR) fashion (as described in Section 2.1).…”

Section: A Filter Combining All Three Methodsmentioning

confidence: 99%

“…Our approach relies on estimating the noise STD as a function of the pixel intensity (i.e. = f(I,p), where I is the local pixel intensity and p is the noise parameters) directly from the given rendered image [8]. Note that if this function is available from a model or off-line measurements then this step can be skipped.…”

Section: Intensity-dependent Filtering For Non-uniform Stdmentioning

confidence: 99%

“…To estimate p that model the intensity dependence, we bucket the image pixels into S intensity bins (e.g., 1~51, 52~102, 103~153, 154~204 and 205~255 for S=5). For each bin, a single noise STD is estimated for the pixels in the bin using the method detailed in [8]. We assume that the noise STD varies slowly with intensity and that the pixels in each bin have similar noise STD values.…”

Section: I+p 2 Imentioning

confidence: 99%

“…The photometric distance function with both the luminance-guidance and the intensity dependence term is . (8) Note that the function f( ) is a function of the luminance intensities instead of the chrominance intensities. As the noise STD varies with the amount of photons captured at each pixel in the image sensor, the chrominance STD is better modeled as a function of the luminance intensities than the chrominance intensities.…”

Section: ( Cb(i)-cb(i-j) Y*(i)-y*(i-j)mentioning

confidence: 99%

See 3 more Smart Citations

Denoising scheme for realistic digital photos from unknown sources

Lim

Maurer

Kisilev

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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Section: A Filter Combining All Three Methodsmentioning

confidence: 99%

Section: Intensity-dependent Filtering For Non-uniform Stdmentioning

confidence: 99%

Section: I+p 2 Imentioning

confidence: 99%

Section: ( Cb(i)-cb(i-j) Y*(i)-y*(i-j)mentioning

confidence: 99%

See 2 more Smart Citations

Denoising scheme for realistic digital photos from unknown sources

Lim

Maurer

Kisilev

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

Self Cite

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show abstract

“…One potential choice to solve this denoising problem is the spatial-domain method proposed by Kisilev, Shaked and Hwan Li [13], which claims that the visual perception of noise depends on both the noise and the underlying image content or signal activity. Based on this argument, known as the masking effect, they introduced a pixelwise noise threshold which is proportional to the ratio of the noise standard deviation to the signal activity.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-based noise-model driven denoising algorithm for differential phase contrast mammography

2013

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Abstract:Traditional mammography can be positively complemented by phase contrast and scattering x-ray imaging, because they can detect subtle differences in the electron density of a material and measure the local small-angle scattering power generated by the microscopic density fluctuations in the specimen, respectively. The grating-based x-ray interferometry technique can produce absorption, differential phase contrast (DPC) and scattering signals of the sample, in parallel, and works well with conventional X-ray sources; thus, it constitutes a promising method for more reliable breast cancer screening and diagnosis. Recently, our team proved that this novel technology can provide images superior to conventional mammography. This new technology was used to image whole native breast samples directly after mastectomy. The images acquired show high potential, but the noise level associated to the DPC and scattering signals is significant, so it is necessary to remove it in order to improve image quality and visualization. The noise models of the three signals have been investigated and the noise variance can be computed. In this work, a wavelet-based denoising algorithm using these noise models is proposed. It was evaluated with both simulated and experimental mammography data. The outcomes demonstrated that our method offers a good denoising quality, while simultaneously preserving the edges and important structural features. Therefore, it can help improve diagnosis and implement further post-processing techniques such as fusion of the three signals acquired.

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OSCAR: On-Site Composition and Aesthetics Feedback Through Exemplars for Photographers

et al. 2011

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In this paper we describe a comprehensive system to enhance the aesthetic quality of the photographs captured by the mobile consumers. The system, named OS-CAR, has been designed to provide on-site composition and aesthetics feedback through retrieved examples. We introduce three novel interactive feedback components. The first is the composition feedback which is qualitative in nature and responds by retrieving highly aesthetic exemplar images from the corpus which are similar in content and composition to the snapshot. The second is the color combination feedback which provides confidence on the snapshot to contain good color combinations. The third component is the overall aesthetics feedback which predicts the aesthetic ratings for both color and monochromatic images. An existing algorithm is used to provide ratings for color images, while new features and a new model are developed to treat monochromatic images. This system was designed keeping the next generation photography needs in mind and is the first of its kind. The feedback rendered is guiding and intuitive in nature. It is computed in situ while requiring minimal input from the user.

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Noise and Signal Activity Maps for Better Imaging Algorithms

Cited by 8 publications

References 5 publications

Denoising scheme for realistic digital photos from unknown sources

Denoising scheme for realistic digital photos from unknown sources

Wavelet-based noise-model driven denoising algorithm for differential phase contrast mammography

OSCAR: On-Site Composition and Aesthetics Feedback Through Exemplars for Photographers

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