Results 1  10
of
29
Recoloring images for gamuts of lower dimension
 Computer Graphics Forum
, 2005
"... Color images have a gamut that typically spans three dimensions. Nevertheless, several important applications, such as the creation of grayscale images for printing and the recoloring ofimagesforcolordeficient viewers, require a reduction of gamut dimension. This paper describes a technique for pr ..."
Abstract

Cited by 40 (1 self)
 Add to MetaCart
(Show Context)
Color images have a gamut that typically spans three dimensions. Nevertheless, several important applications, such as the creation of grayscale images for printing and the recoloring ofimagesforcolordeficient viewers, require a reduction of gamut dimension. This paper describes a technique for preserving visual detail while reducing gamut dimension. The technique is derived by focusing on the problem of converting color imagestograyscale. A straightforward extension is then provided that allows recoloring images for colordeficient viewers. Care is taken so that the resulting images remain within the available gamut and visual artifacts are not introduced.
Image fusion with guided filtering
 IEEE Trans. Image Process
, 2013
"... Abstract — A fast and effective image fusion method is proposed for creating a highly informative fused image through merging multiple images. The proposed method is based on a twoscale decomposition of an image into a base layer containing large scale variations in intensity, and a detail layer ca ..."
Abstract

Cited by 14 (1 self)
 Add to MetaCart
(Show Context)
Abstract — A fast and effective image fusion method is proposed for creating a highly informative fused image through merging multiple images. The proposed method is based on a twoscale decomposition of an image into a base layer containing large scale variations in intensity, and a detail layer capturing small scale details. A novel guided filteringbased weighted average technique is proposed to make full use of spatial consistency for fusion of the base and detail layers. Experimental results demonstrate that the proposed method can obtain stateoftheart performance for fusion of multispectral, multifocus, multimodal, and multiexposure images. Index Terms — Guided filter, image fusion, spatial consistency, twoscale decomposition. I.
ClusterBased Color Space Optimizations
"... Transformations between different color spaces and gamuts are ubiquitous operations performed on images. Often, these transformations involve information loss, for example when mapping from color to grayscale for printing, from multispectral or multiprimary data to tristimulus spaces, or from one co ..."
Abstract

Cited by 9 (1 self)
 Add to MetaCart
(Show Context)
Transformations between different color spaces and gamuts are ubiquitous operations performed on images. Often, these transformations involve information loss, for example when mapping from color to grayscale for printing, from multispectral or multiprimary data to tristimulus spaces, or from one color gamut to another. In all these applications, there exists a straightforward “natural ” mapping from the source space to the target space, but the mapping is not bijective, resulting in information loss due to metamerism and similar effects. We propose a clusterbased approach for optimizing the transformation for individual images in a way that preserves as much of the information as possible from the source space while staying as faithful as possible to the natural mapping. Our approach can be applied to a host of color transformation problems including color to gray, gamut mapping, conversion of multispectral and multiprimary data to tristimulus colors, and image optimization for color deficient viewers. 1.
Image fusion and enhancement via empirical mode decomposition
 Journal of Pattern Recognition Research
"... www.jprr.org ..."
Fast colour2grey
 In 16th Color Imaging Conference: Color, Science, Systems and Applications
, 2008
"... A standard approach to generating a greyscale equivalent to an input colour image involves calculating the socalled structure tensor at each image pixel. Defining contrast as associated with the maximumchange direction of this matrix, the grey gradient is identified with the first eigenvector direc ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
(Show Context)
A standard approach to generating a greyscale equivalent to an input colour image involves calculating the socalled structure tensor at each image pixel. Defining contrast as associated with the maximumchange direction of this matrix, the grey gradient is identified with the first eigenvector direction, with gradient strength given by the square root of its eigenvalue. However, aside from the inherent complexity of such an approach, each pixel’s gradient still possesses a sign ambiguity, since an eigenvector is given only up to a sign. This is ostensibly resolved by looking at how one of the R,G,B colour channels behaves, or how the the luminance changes. Instead, we would like to circumvent the sign problem in the first place, and also avoid calculating the costly eigenvector decomposition. So here we suggest replacing the eigenvector approach by generating a greyscale gradient equal to the maximum gradient amongst the R,G,B gradients, in each of x, y. But in order not to neglect the tensor approach, we consider the relationship between the complex and the simple approaches. We also note that, at each pixel, we have both forwardfacing and backwardfacing derivatives, which are different, and we consider a tensor formed from both. Then, over a standard training set, we ask for an optimum set of weights for all the maximum gradients such that the simple maxima scheme generates a greyscale structure tensor to best match the original, colour, one. We find that a simple scheme that facilitates fast solutions is best. Greyscale results are shown to be excellent, and the algorithm is very fast. 1.
Interactive Visualization of Hyperspectral Images of Historical Documents
"... Abstract—This paper presents an interactive visualization tool to study and analyze hyperspectral images (HSI) of historical documents. This work is part of a collaborative effort with the Nationaal Archief of the Netherlands (NAN) and Art Innovation, a manufacturer of hyperspectral imaging hardware ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
(Show Context)
Abstract—This paper presents an interactive visualization tool to study and analyze hyperspectral images (HSI) of historical documents. This work is part of a collaborative effort with the Nationaal Archief of the Netherlands (NAN) and Art Innovation, a manufacturer of hyperspectral imaging hardware designed for old and fragile documents. The NAN is actively capturing HSI of historical documents for use in a variety of tasks related to the analysis and management of archival collections, from ink and paper analysis to monitoring the effects of environmental aging. To assist their work, we have developed a comprehensive visualization tool that offers an assortment of visualization and analysis methods, including interactive spectral selection, spectral similarity analysis, timevarying data analysis and visualization, and selective spectral band fusion. This paper describes our visualization software and how it is used to facilitate the tasks needed by our collaborators. Evaluation feedback from our collaborators on how this tool benefits their work is included. Index Terms—Hyperspectral visualization, data exploration, image fusion, document processing and analysis 1
G.: Realistic colorization via the structure tensor
 In: International Conference on Image Processing, ICIP08 (2008
"... Colorization is a userassisted color manipulation mechanism for changing grayscale images into colored ones. Several colorization algorithms have been constructed, and these methods are able to produce appropriately colorized images given a surprisingly sparse set of hints supplied by the user. But ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
(Show Context)
Colorization is a userassisted color manipulation mechanism for changing grayscale images into colored ones. Several colorization algorithms have been constructed, and these methods are able to produce appropriately colorized images given a surprisingly sparse set of hints supplied by the user. But these color images may not in fact look realistic. Moreover, the contrast in the colorized image may not match the gradient perceived in the original grayscale image. We argue that it is this departure from the original gradient that contributes to the unreal appearance in some colorizations. To correct this, we make use of the Di Zenzo gradient of a color image derived from the structure tensor, and adjust the colorized correlate such that the Di Zenzo definition of the maximumcontrast gradient agrees with the gradient in the original gray image. This tends to result in more naturallooking images in color. In particular, “hotspots ” of unrealistic color are subdued into regions of more realistic color. Index Terms — Colorization, Di Zenzo, gradient, contrast 1.
Fast Multispectral2Gray
"... A standard approach to generating a grayscale equivalent to an input multispectral image involves calculating the socalled structure tensor at each image pixel. Defining contrast as associated with the maximumchange direction of this matrix, the gray gradient is identified with the first eigenvec ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
A standard approach to generating a grayscale equivalent to an input multispectral image involves calculating the socalled structure tensor at each image pixel. Defining contrast as associated with the maximumchange direction of this matrix, the gray gradient is identified with the first eigenvector direction, with gradient strength given by the square root of its eigenvalue. However, aside from the inherent complexity of such an approach, each pixel’s gradient still possesses a sign ambiguity, since an eigenvector is given only up to a sign. This is ostensibly resolved by looking at how one of the color channels behaves, or how the the luminance changes, or how overall integrability is affected by each sign choice. Instead, we would like to circumvent the sign problem in the first place, and also avoid calculating the costly eigenvector decomposition. We suggest replacing the eigenvector approach by generating a grayscale gradient equal to the maximum gradient amongst the color or multispectral channels ’ gradients, in each of x, y. color or But in order not to neglect the tensor approach, we consider the relationship between the complex and the simple approaches. We also note that, at each pixel, we have both forwardfacing and backwardfacing derivatives, which are different. In a novel approach, we consider a tensor formed from both. Then, over a standard training set, we ask for an optimum set of weights for all the maximum gradients such that the simple maxima scheme generates a grayscale structure tensor to best match the original, multispectral, one. If we use only forwardfacing derivatives, a fast Fourierbased solution is possible. But instead, we find that a simple scheme that equally weights maxima in the forwardfacing and backwardfacing directions produces superlative results if a reset step is included, in a spatialdomain solution. Grayscale results are shown to be excellent, and the algorithm is very fast. 1.
Fisher Information and the Combination of RGB Channels
 Proc. CCIW
, 2013
"... Abstract. We introduce a method to combine the color channels of an image to a scalar valued image. Linear combinations of the RGB channels are constructed using the FisherTraceInformation (FTI), defined as the trace of the Fisher information matrix of the Weibull distribution, as a cost functio ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
(Show Context)
Abstract. We introduce a method to combine the color channels of an image to a scalar valued image. Linear combinations of the RGB channels are constructed using the FisherTraceInformation (FTI), defined as the trace of the Fisher information matrix of the Weibull distribution, as a cost function. The FTI characterizes the local geometry of the Weibull manifold independent of the parametrization of the distribution. We show that minimizing the FTI leads to contrast enhanced images, suitable for segmentation processes. The Riemann structure of the manifold of Weibull distributions is used to design optimization methods for finding optimal weight RGB vectors. Using a threshold procedure we find good solutions even for images with limited content variation. Experiments show how the method adapts to images with widely varying visual content. Using these image dependent decolorizations one can obtain substantially improved segmentation results compared to a mapping with predefined coefficients.
SALIENCE PRESERVING IMAGE FUSION WITH DYNAMIC RANGE COMPRESSION
"... Gradient conveys important salient features in images. Traditional fusion methods based on gradient generally treat gradients from multichannels as a multivalued vector, and compute its global statistics under the assumption of identical distribution. However, different source channels may reflect ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
Gradient conveys important salient features in images. Traditional fusion methods based on gradient generally treat gradients from multichannels as a multivalued vector, and compute its global statistics under the assumption of identical distribution. However, different source channels may reflect different important salient features, and their gradients are basically nonidentically distributed. This prevents existing methods from successful salience preservation. In this paper, we propose to fuse the gradients from multichannels in the concept of saliency. We first measure the salience map of each channel’s gradient, and then use their saliency to weight their contribution in computing the global statistics. Gradients with high saliency are properly highlighted in the target gradient, and thereby salient features in the sources are well preserved. Furthermore, we handle the dynamic range problem by applying range compression on the target gradient, and thereby halo effect is effectively reduced. 1.