Abstract
A new framework for image representation, processing, and analysis is introduced and exposed through practical applications. The proposed approach is called logarithmic adaptive neighborhood image processing (LANIP) since it is based on the logarithmic image processing (LIP) and on the general adaptive neighborhood image processing (GANIP) approaches, that allow several intensity and spatial properties of the human brightness perception to be mathematically modeled and operationalized, and computationally implemented. The LANIP approach is mathematically, computationally, and practically relevant and is particularly connected to several human visual laws and characteristics such as: intensity range inversion, saturation characteristic, Webers and Fechners laws, psychophysical contrast, spatial adaptivity, multiscale adaptivity, morphological symmetry property. The LANIP approach is finally exposed in several areas: image multiscale decomposition, image restoration, image segmentation, and image enhancement, through biomedical materials and visual imaging applications.
Highlights
In its broad acceptation [1], the notion of processing an image involves the transformation of that image from one form into another
The main aim of this multiscale process is to highlight the stroke area, in order to help the neurologist for the diagnosis of the kind of stroke, and/or to allow a robust segmentation to be performed. These results show the advantages of spatial adaptivity and intrinsic multiscale analysis of the logarithmic adaptive neighborhood image processing (LANIP)-based operators
The LANIP approach is a combination of the logarithmic image processing (LIP) [21] and the general adaptive neighborhood image processing (GANIP) [34] frameworks: LANIP = LIP + GANIP
Summary
In its broad acceptation [1], the notion of processing an image involves the transformation of that image from one form into another. The result may be a new image or may take the form of an abstraction, parametrization, or a decision. Image processing is a large and interdisciplinary field which deals with images. Within the scope of the present article, the term image will refer to a continuous or discrete (including the digital form) two-dimensional distribution of light intensity [2, 3], considered either in its physical or in its psychophysical form
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