摘要
为了研究基于人类视觉系统特性(亮度自适应特性和视网膜神经元感受野非经典侧抑制特性)的仿生图像处理方法,增强仿生图像增强算法的自适应性,提出了一种新的自适应仿生图像增强算法——LDRF算法.LDRF算法首先建立参数对数模型对图像全局亮度进行自适应调整,然后采用三高斯动态滤波进行局部细节增强,引入Wal-lis算子建立增益因子模型,使局部细节增强具有自适应性,最后通过线性变换恢复图像彩色信息.在大量图像上进行对比实验和分析.实验结果证明,LDRF算法能够避免过增强现象,并且针对不同大小、不同内容的图像能够自适应地进行图像增强,取得了较好的效果,提高了仿生图像增强算法的实用性.
The research proposes to continue examining a novel adaptive biomimetic image processing method called, LDRF （logarithmic and disinhibitory properties of concentric receptive field） algorithm. Numerous research studies have examined biomimetie image processing method and human visual characteristics, which include brightness adaptability and disinhibitory properties of concentric receptive fields. Firstly, a parameterize logarithmic function was adjusted for global luminance for image adaptability. Secondly, the tri-Gaussian dynamic filtering was applied to the partial detail en hancement. Wallis operator was also introduced for establishing a gain factor model that provided adaptability for partial detail enhancement as well. Finally, the linear transform was performed for the color restoration. The contrast experi mental results were based on a large image set which indicated, the LDRF algorithm is capable of adapting and enhan cing the images with different sizes and contents. Additionally, the study avoided over enhancement, achieved excellent effects and increased practicality of the biomimetic image enhancement algorithm.
出处
《智能系统学报》
北大核心
2012年第5期404-408,共5页
CAAI Transactions on Intelligent Systems
基金
国家自然科学基金重大研究项目(90920013)
关键词
仿生图像处理
人类视觉
图像增强
三高斯模型
参数对数模型
增益因子模型
biomimetic image processing
human visual system
image enhancement
tri-Guassian model
parameterizedlogm＇ithmic model
gain factor model
