Recovering real-world images from single-scale boundaries with a novel filling-in architecture
- 作者：Matthias S. Keil ; Gabriel Cristó ; bal ; Thorsten Hansen and Heiko Neumann
- 关键词：Filling-in models ; Real-world image ; BEATS filling-in
- 刊名：Neural Networks
- 出版年：2005
- 期刊代码：94_08936080
- 类别：cp
- 出版时间：December 2005
- 卷：18
- 期：10
- 页码：1319-1331
- 文件大小：802 K
摘要
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.