Abstract
A perceptual lightness anchoring model based on visual cognition is proposed. It can recover absolute lightness of natural images using filling-in mechanism from single-scale boundaries. First, it adapts the response of retinal photoreceptors to varying levels of illumination. Then luminance-correlated contrast information can be obtained through multiplex encoding without additional luminance channel. Dynamic normalization is used to get smooth and continuous boundary contours. Different boundaries are used for ON and OFF channel diffusion layers. Theoretical analysis and simulation results indicate that the model could recover natural images under varying illumination, and solve the trapping, blurring and fogging problems to some extent.
Highlights
Studies show that human could perceive a wide dynamic range of lightness from dim moonlight to dazzling sunlight
A perceptual lightness anchoring model based on visual cognition is proposed
In order to validate the effectiveness of the proposed model, we first evaluate the performance of each stage, the lightness perception performance of the whole model is tested using natural images
Summary
Studies show that human could perceive a wide dynamic range of lightness from dim moonlight to dazzling sunlight. Luminance values in the retinal image are a product, of the actual physical shade of gray of the imaged surfaces, and, and even more so, of the intensity of the light illuminating those surfaces. The luminance of any region of the retinal image can vary by a factor of no more than thirty to one as a function of the physical reflectance of that surface. BCS/FCS (Boundary Contour System/Feature Contour System) proposed by Grossberg et al is representative of visual lightness perception model. Further processing is made by visual cortex to get boundary contour and surface Such illumination discounting information can just estimate relative measurements of reflectance of the surface. The proposed model could recover natural images under varying levels of illumination, and solve the trapping, blurring and fogging problems to some extent
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
