摘要
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.
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.
