Abstract
Image enhancement is an important pre-processing step for various image processing applications. In this paper, we proposed a physiologically-based adaptive three-Gaussian model for image enhancement. Comparing to the standard three-Gaussian model inspired by the spatial structure of the receptive field (RF) of the retinal ganglion cells, the proposed model can dynamically adjust its parameters according to the local image luminance and contrast based on the physiological findings. Experimental results on several images show that the proposed adaptive three-Gaussian model achieves better performance than the classical method of histogram equalization and the standard three-Gaussian model.
Highlights
Images play an important role in transferring information
Traditional image enhancement methods can be roughly divided into two categories: 1) spatial domain methods, such as gray-level transformation, piecewise-linear transformation, and histogram equalization, etc. 2) frequency domain methods
+ A3e σ where A1 and σ1 are the strength and space constant of the excitatory center, A2 and σ 2 are the strength and space constant of the inhibitory surround, and A3 and σ3 are the strength and space constant of the disinhibitory outer-surround region. This three-Gaussian model assumes that the sensitivity profiles of the three regions are distributed as Gaussians, which are circularly concentric with their peaks overlapped at the center point of the receptive field (RF) center region
Summary
Images play an important role in transferring information. In order to obtain more information from collected images, image enhancement techniques are commonly required to improve image quality. These methods mentioned above are in general difficult to balance well among various requirements of image quality, such as contour enhancement, dynamic range, denoising and so on. By analyzing the length-response functions of lateral geniculate neurons in the cat, Li et al have demonstrated an extensive disinhibitory region (DIR, i.e., non-CRF) outside the classical inhibitory surround of the receptive field [4] According to this finding, a three-Gaussian function model was proposed in [5]. To simulate the dynamic properties of the RF, in this work we present an adaptive three-Gaussian function model to automatically adjust its parameters according to the properties of local stimulus, i.e., local contrast and luminance, for image enhancement
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