General Adaptive Neighborhood Image Restoration, Enhancement and Segmentation

Debayle, Johan; Gavet, Yann; Pinoli, Jean‐Charles

doi:10.1007/11867586_3

Cited by 11 publications

(11 citation statements)

References 19 publications

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“…However, these techniques avoid the problem of detecting the cells since they are only focusing on an average cell density computation. Some other methods try to mimic the human visual perception by detecting the borders of the cells (Gain et al, 2002;de Andrade, 2004;Debayle et al, 2006), or by extracting the cell bodies (Foracchia and Ruggeri, 2000).…”

Section: The Limits Of Existing Methodsmentioning

confidence: 99%

Visual Perception Based Automatic Recognition of Cell Mosaics in Human Corneal Endotheliummicroscopy Images

Gavet

Pinoli

2011

Image Anal Stereol

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The human corneal endothelium can be observed with two types of microscopes: classical optical microscope for ex-vivo imaging, and specular optical microscope for in-vivo imaging. The quality of the cornea is correlated to the endothelial cell density and morphometry. Automatic methods to analyze the human corneal endothelium images are still not totally efficient. Image analysis methods that focus only on cell contours do not give good results in presence of noise and of bad conditions of acquisition. More elaborated methods introduce regional informations in order to performthe cell contours completion, thus implementing the duality contour-region. Their good performance can be explained by their connections with several basic principles of human visual perception (Gestalt Theory and Marr's computational theory)

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Section: The Limits Of Existing Methodsmentioning

confidence: 99%

Visual Perception Based Automatic Recognition of Cell Mosaics in Human Corneal Endotheliummicroscopy Images

Gavet

Pinoli

2011

Image Anal Stereol

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“…Efficient operators have been developed and studied [8], [37] for multiscale image decomposition, image background subtraction, image restoration, image enhancement, image segmentation [20], [38], [39] in the fields of material and biomedical engineering. …”

Section: Ganip Tools and Applicationsmentioning

confidence: 99%

Spatially and Intensity Adaptive Morphology

Pinoli

Debayle

2012

IEEE J. Sel. Top. Signal Process.

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International audienceIn this paper, spatially and intensity adaptive morphology is introduced and studied in the context of the General Adaptive Neighborhood Image Processing (GANIP) approach. The combination of GAN (General Adaptive Neighborhood)-based filtering and semi-flat morphology is particularly efficient in the sense that the filtering is adaptive to the image spatial structures (structuring elements are spatially variant) and its activity is controlled according to the image intensities (level sets are processed at different scales). The resulting morphological filters show a high image processing performance while preserving the image regions and details without damaging its transitions. The effectiveness of these adaptive operators are practically highlighted on real application examples for image background removing, image restoration and image enhancement

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“…The model has also shown to satisfy Weber's Law, which quantifies the human eye's ability to perceive intensity differences for a given background intensity [18]. As a result, image enhancement [19], edge detection [20], and image restoration [21] algorithms utilizing the LIP model have yielded better results.…”

Section: Introductionmentioning

confidence: 99%

Multiresolution Decomposition Schemes Using the Parameterized Logarithmic Image Processing Model with Application to Image Fusion

Nercessian

Panetta

Agaian

2010

EURASIP J. Adv. Signal Process.

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New pixel-and region-based multiresolution image fusion algorithms are introduced in this paper using the Parameterized Logarithmic Image Processing (PLIP) model, a framework more suitable for processing images. A mathematical analysis shows that the Logarithmic Image Processing (LIP) model and standard mathematical operators are extreme cases of the PLIP model operators. Moreover, the PLIP model operators also have the ability to take on cases in between LIP and standard operators based on the visual requirements of the input images. PLIP-based multiresolution decomposition schemes are developed and thoroughly applied for image fusion as analysis and synthesis methods. The new decomposition schemes and fusion rules yield novel image fusion algorithms which are able to provide visually more pleasing fusion results. LIP-based multiresolution image fusion approaches are consequently formulated due to the generalized nature of the PLIP model. Computer simulations illustrate that the proposed image fusion algorithms using the Parameterized Logarithmic Laplacian Pyramid, Parameterized Logarithmic Discrete Wavelet Transform, and Parameterized Logarithmic Stationary Wavelet Transform outperform their respective traditional approaches by both qualitative and quantitative means. The algorithms were tested over a range of different image classes, including out-of-focus, medical, surveillance, and remote sensing images.

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General Adaptive Neighborhood Image Restoration, Enhancement and Segmentation

Cited by 11 publications

References 19 publications

Visual Perception Based Automatic Recognition of Cell Mosaics in Human Corneal Endotheliummicroscopy Images

Visual Perception Based Automatic Recognition of Cell Mosaics in Human Corneal Endotheliummicroscopy Images

Spatially and Intensity Adaptive Morphology

Multiresolution Decomposition Schemes Using the Parameterized Logarithmic Image Processing Model with Application to Image Fusion

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