Analog VLSI and neural systems
Analog VLSI and neural systems
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analog hardware for detecting discontinuities in early vision
International Journal of Computer Vision
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Temporal photoreception for adaptive dynamic range image sensing and encoding
Neural Networks - Special issue on neural control and robotics: biology and technology
LCIS: a boundary hierarchy for detail-preserving contrast reduction
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Multigrid
Robot Vision
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
Adaptive gain control for high dynamic range image display
SCCG '02 Proceedings of the 18th spring conference on Computer graphics
Tone Reproduction for Realistic Images
IEEE Computer Graphics and Applications
Generalized Mosaicing: High Dynamic Range in a Wide Field of View
International Journal of Computer Vision
Neural Mechanisms for Representing Surface and Contour Features
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
Parallel Networks for Machine Vision
Parallel Networks for Machine Vision
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Adaptive Dynamic Range Imaging: Optical Control of Pixel Exposures Over Space and Time
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A multiscale retinex for bridging the gap between color images and the human observation of scenes
IEEE Transactions on Image Processing
Total Variation Models for Variable Lighting Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive dynamic range camera with reflective liquid crystal
Journal of Visual Communication and Image Representation
Lighting-by-Example with Wavelets
SG '07 Proceedings of the 8th international symposium on Smart Graphics
Variable lighting face recognition using discrete wavelet transform
Pattern Recognition Letters
Ghost detection and removal for high dynamic range images: Recent advances
Image Communication
Automatic exposure correction of consumer photographs
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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Many computer vision applications have to cope with large dynamic range and changing illumination conditions in the environment. Any attempt to deal with these conditions at the algorithmic level alone are inherently difficult because: 1) conventional image sensors cannot completely capture wide dynamic range radiances without saturation or underexposure, 2) the quantization process destroys small signal variations especially in shadows, and 3) all possible illumination conditions cannot be completely accounted for. The paper proposes a computational model for brightness perception that deals with issues of dynamic range and noise. The model can be implemented on-chip in analog domain before the signal is saturated or destroyed through quantization. The model is "unified" because a single mathematical formulation addresses the problem of shot and thermal noise, and normalizes the signal range to simultaneously 1) compress the dynamic range, 2) minimize appearance variations due to changing illumination, and 3) minimize quantization noise.The model strongly mimics brightness perception processes in early biological vision.