Adaptive histogram equalization and its variations
Computer Vision, Graphics, and Image Processing
Contrast enhancement technique based on local detection of edges
Computer Vision, Graphics, and Image Processing
Image contrast enhancement based on the intensities of edge pixels
CVGIP: Graphical Models and Image Processing
Generation of transfer functions with stochastic search techniques
Proceedings of the 7th conference on Visualization '96
A non-photorealistic lighting model for automatic technical illustration
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Digital Image Processing
Maximum entropy light source placement
Proceedings of the conference on Visualization '02
Edge-Affected Context for Adaptive Contrast Enhancement
IPMI '91 Proceedings of the 12th International Conference on Information Processing in Medical Imaging
Viewpoint Selection using Viewpoint Entropy
VMV '01 Proceedings of the Vision Modeling and Visualization Conference 2001
Histogram modification via partial differential equations
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Histogram Equalization using Neighborhood Metrics
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Fusing features in direct volume rendered images
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Shape preserving local histogram modification
IEEE Transactions on Image Processing
Adaptive image contrast enhancement using generalizations of histogram equalization
IEEE Transactions on Image Processing
Gray and color image contrast enhancement by the curvelet transform
IEEE Transactions on Image Processing
Hue-preserving color image enhancement without gamut problem
IEEE Transactions on Image Processing
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In this paper, we propose a new method for enhancing the quality of direct volume rendered images. Unlike the typical image enhancement techniques which perform transformations in the image domain, we take the volume data into account and enhance the presentation of the volume in the rendered image by adjusting the rendering parameters. Our objective is not only to deliver a pleasing image with better color contrast or enhanced features, but also generate a faithful image with the information in the volume presented in the image. An image quality measurement is proposed to quantitatively evaluate image quality based on the information obtained from the image as well as the volumetric data. The parameter adjustment process is driven by the evaluation result using a genetic algorithm. More informative and comprehensible results are therefore delivered, compared with the typical image-based approaches.