Research On Several Image Processing Problems As The Simulation Of Vision Mechanisms

Human visual system (HVS) possesses very excellent image processing abilities.Based on the extensive acceptant visual information processing flows, this papermimicks neural mechanisms of retina, lateral geniculate nucleus, visual cortex and moreabstract cerebral cortex,and propose some image processing methods equipped with theabove neural mechanisms to face some practical image processing problems. It mainlyinvolves:1. We propose two models to solve the color constancy problems. One modelnamed "single-opponent cell with non-classical receptive field (nCRF)"(SONRF), it isproposed as a potential mechanism underlying image color constancy at the level ofretinal ganglion (RG) cells and lateral geniculate nucleus (LGN) neurons. This modelsimulates the properties of inhibitory interactions among the subunits of the nCRF (i.e.,disinhibitory effects) and the inhibitory modulation of the subunits to the center with thecolor opponent mechanism of red-green, green-red and blue-yellow. Another model, bymimicking receptive fields of the primary visual cortical neurons from the views ofimage derivative and non-negative sparse coding, is a color constancy model combiningwith the image derivative framework and non-negative sparse coding. We employcommonly used color image databases to quantitatively evaluate these two models,which gave the comparable results as the state-of-the-art non-biologically inspired colorconstancy algorithms. In the view of image processing, these results demonstrate theutility and potential applications of algorithms inspired by biological mechanisms incomputer vision and other realms of image processing. In the view of neuroscience,those models provide supports for the notion of subcortical neurons and primary visualcortex's roles on the capacity of color constancy.2. As HVS has the properties of feature detection abilities, hierarchy, bidirectionalconnection, and self-learning mechanisms, etc, we propose a multiscale Markov randomfield model in the wavelet domain by simulating some functions of HVS for imagesegmentation. Concretely, for an input scene, using wavelet transforms, our model provides its sparse representations to mimic feature detection abilities, and using thepyramid framework, our model mimics hierarchy. In the framework of our model, thereare two information flows imitating bidirectional connection. For example, a bottom-upprocedure is adopted to extract input features and a top-down procedure is used toprovide feedback controls. Moreover, iterations are the simulation of self-learningmechanisms. In addition, setted by different parameters, our model is able to excuatedifferent biomedical image segmentation tasks, such as edge detection and regionsegmentation with pixels classification.3. A model mimicking cortex rhythms is adopted to achieve image enhancement.This model is based on the coupled Wilson-Cowan oscillators with double nodes. Theinputs are images to be enhanced and the outputs are node responses of the excitedsubpopulation. As image experiments show, the method is able to be used in imageenhancement. Meanwhile, we found that if image patches with continuous gray valuesare employed as stimulus, the response curves are similar with the classical Gammacorrection curves that are used in image enhancement. This fact on one side providesevidence of the image enhancement ability of the proposed method, on the other side, itprovides the neural mechanism, oscillation of the neural population, of the classicalGamma correction method. Numerically compared with the receptive field model basedclassical center-surround Retinex algorithm, the new method shows better results.