Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Competitive learning algorithms for vector quantization
Neural Networks
Vector quantization and signal compression
Vector quantization and signal compression
A contrast-based scalefactor for luminance display
Graphics gems IV
A model of visual adaptation for realistic image synthesis
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
A multiscale model of adaptation and spatial vision for realistic image display
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Two methods for display of high contrast images
ACM Transactions on Graphics (TOG)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Gradient domain high dynamic range compression
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Fast bilateral filtering for the display of high-dynamic-range images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Photographic tone reproduction for digital images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
A tone mapping algorithm for high contrast images
EGRW '02 Proceedings of the 13th Eurographics workshop on Rendering
A Visibility Matching Tone Reproduction Operator for High Dynamic Range Scenes
IEEE Transactions on Visualization and Computer Graphics
Tone Reproduction for Realistic Images
IEEE Computer Graphics and Applications
ACM SIGGRAPH 2003 Papers
Perception-motivated high dynamic range video encoding
ACM SIGGRAPH 2004 Papers
High dynamic range display systems
ACM SIGGRAPH 2004 Papers
Tone-mapping high dynamic range images by novel histogram adjustment
Pattern Recognition
Segmentation based tone-mapping for high dynamic range images
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
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In this paper, we present a learning-based image processing technique. We have developed a novel method to map high dynamic range scenes to low dynamic range images for display in standard (low dynamic range) reproduction media. We formulate the problem as a quantization process and employ an adaptive conscience learning strategy to ensure that the mapped low dynamic range displays not only faithfully reproduce the visual features of the original scenes, but also make full use of the available display levels. This is achieved by the use of a competitive learning neural network that employs a frequency sensitive competitive learning mechanism to adaptively design the quantizer. By optimizing an L"2 distortion function, we ensure that the mapped low dynamic images preserve the visual characteristics of the original scenes. By incorporating a frequency sensitive competitive mechanism, we facilitate the full utilization of the limited displayable levels. We have developed a deterministic and practicable learning procedure which uses a single variable to control the display result. We give a detailed description of the implementation procedure of the new learning-based high dynamic range compression method and present experimental results to demonstrate the effectiveness of the method in displaying a variety of high dynamic range scenes.