Recovering real-world images from single-scale boundaries with a novel filling-in architecture.
Filling-in models were successful in predicting psychophysical data for brightness perception. Nevertheless, their suitability for real-world image processing has never been examined. A unified architecture for both predicting psychophysical data and real-world image processing would constitute a powerful theory for early visual information processing. As a first contribution of the present paper, we identified three principal problems with current filling-in architectures, which hamper the goal of having such a unified architecture. To overcome these problems we propose an advance to filling-in theory, called BEATS filling-in, which is based on a novel nonlinear diffusion operator. BEATS filling-in furthermore introduces novel boundary structures. We compare, by means of simulation studies with real-world images, the performance of BEATS filling-in with the recently proposed confidence-based filling-in. As a second contribution we propose a novel mechanism for encoding luminance information in contrast responses ('multiplex contrasts'), which is based on recent neurophysiological findings. Again, by simulations, we show that 'multiplex contrasts' at a single, high-resolution filter scale are sufficient for recovering absolute luminance levels. Hence, 'multiplex contrasts' represent a novel theory addressing how the brain encodes and decodes luminance information.
Keil
Matthias S
MS
Centre de Visió per Computador, Edifici O, Campus UAB, E-08193 Bellaterra, Cerdanyola, Barcelona, Spain. mats@cvc.uab.es
Cristóbal
Gabriel
G
Hansen
Thorsten
T
Neumann
Heiko
H
D003198:Computer Simulation; D003199:Computer Systems; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008027:Light; D008959:Models, Neurological; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D011601:Psychophysics; D012160:Retina / Q000502:physiology; D014796:Visual Perception