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基于视觉神经元ON―OFF模型的图象增强

DOI: 10.11834/jig.200305176

Keywords: 计算机图象处理(520?6040),图象增强,ON-OFF神经网络

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Abstract:

探讨基于视觉神经网络ON-OFF模型的图象增强.通过计算仿真,找到了关于这种视觉模型应用于图象处理合适的实现形式;探讨了衰减常数和颜色恒定性的关系;以及空间常数变化和动态范围压缩与细节增强能力的联系.使用合适的衰减常数,可以使该神经元模型在颜色恒定性计算上性能优良,同时,适当大小的空间常数,能够在颜色保真度和图像增强性能之间取得合适的平衡.神经网络对复杂背景图象的增强效果良好,但是由于同样基于“灰度世界”假设,因此在处理违反这一假设的特殊图象时,此模型在颜色表征上仍然具有缺陷,这就部分限制了模型的应用.最后探讨了可能的改进方向.

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