Book ChapterDOI
Color image enhancement in the framework of logarithmic models
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This work defines some criteria for automatically determining the parameters of the processing and this is done via the fuzzy mean and fuzzy variance computed by logarithmic operations.Abstract:
The logarithmic model offers new tools for image processing. An efficient method for image enhancement, is to use an affine transformation with the logarithmic operations: addition and scalar multiplication. By adding a fuzzy setting to our model we gain flexibility and better results are possible. We define some criteria for automatically determining the parameters of the processing and this is done via the fuzzy mean and fuzzy variance computed by logarithmic operations.read more
Citations
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Book ChapterDOI
Logarithmic image processing: additive contrast, multiplicative contrast, and associated metrics
TL;DR: The concept of logarithmic additive contrast (LAC), its physical interpretation based on transmittance notion and some resulting properties, is introduced, which represents by definition a grey level and it is highly efficient when computed on dark pairs of pixels, with applications for low-lighted images.
Book ChapterDOI
Logarithmic Image Processing for Color Images
TL;DR: The transmittance of color images is defined to provide a physical justification, on which the definition of logarithmic operators such as addition, subtraction, and scalar multiplication are based, respectively noted in the LIPC as c, c , and c .
Journal ArticleDOI
Logarithmic adaptive neighborhood image processing (LANIP): introduction, connections to human brightness perception, and application issues
TL;DR: The logarithmic adaptive neighborhood image processing (LANIP) approach is exposed in several areas: image multiscale decomposition, image restoration, image segmentation, and image enhancement, through biomedical materials and visual imaging applications.
Proceedings ArticleDOI
Distance errors correction for the time of flight (ToF) cameras
D. Falie,V. Buzuloiu +1 more
TL;DR: A model for describing these distance errors and methods for correcting them is proposed and one is simple enough for building it inside camera for real time correction.
Book ChapterDOI
A Pseudo-logarithmic Image Processing Framework for Edge Detection
TL;DR: The paper presents a new [pseudo-] Logarithmic Model for Image Processing (LIP), which allows the computation of gray-level addition, substraction and multiplication with scalars within a fixedgray-level range [0; D ] without the use of clipping.
References
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Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Book
Fundamentals of digital image processing
TL;DR: This chapter discusses two Dimensional Systems and Mathematical Preliminaries and their applications in Image Analysis and Computer Vision, as well as image reconstruction from Projections and image enhancement.
Book
Digital Picture Processing
Azriel Rosenfeld,Avinash C. Kak +1 more
TL;DR: The rapid rate at which the field of digital picture processing has grown in the past five years had necessitated extensive revisions and the introduction of topics not found in the original edition.
Journal ArticleDOI
Efficient color histogram indexing for quadratic form distance functions
TL;DR: In this paper, the authors proposed the use of low-dimensional, simple to compute distance measures between the color distributions, and showed that these are lower bounds on the histogram distance measure.