Ghosh Kuntal, Bhaumik Kamales, Sarkar Sandip
Centre for Soft Computing Research, Indian Statistical Institute, 203, B.T. Road, Calcutta, India.
Prog Brain Res. 2008;168:175-91. doi: 10.1016/S0079-6123(07)68015-7.
The present work is aimed at understanding and explaining some of the aspects of visual signal processing at the retinal level while exploiting the same towards the development of some simple techniques in the domain of digital image processing. Classical studies on retinal physiology revealed the nature of contrast sensitivity of the receptive field of bipolar or ganglion cells, which lie in the outer and inner plexiform layers of the retina. To explain these observations, a difference of Gaussian (DOG) filter was suggested, which was subsequently modified to a Laplacian of Gaussian (LOG) filter for computational ease in handling two-dimensional retinal inputs. Till date almost all image processing algorithms, used in various branches of science and engineering had followed LOG or one of its variants. Recent observations in retinal physiology however, indicate that the retinal ganglion cells receive input from a larger area than the classical receptive fields. We have proposed an isotropic model for the non-classical receptive field of the retinal ganglion cells, corroborated from these recent observations, by introducing higher order derivatives of Gaussian expressed as linear combination of Gaussians only. In digital image processing, this provides a new mechanism of edge detection on one hand and image half-toning on the other. It has also been found that living systems may sometimes prefer to "perceive" the external scenario by adding noise to the received signals in the pre-processing level for arriving at better information on light and shade in the edge map. The proposed model also provides explanation to many brightness-contrast illusions hitherto unexplained not only by the classical isotropic model but also by some other Gestalt and Constructivist models or by non-isotropic multi-scale models. The proposed model is easy to implement both in the analog and digital domain. A scheme for implementation in the analog domain generates a new silicon retina model implemented on a hardware development platform.
本研究旨在理解和解释视网膜层面视觉信号处理的某些方面，同时利用这些方面开发数字图像处理领域的一些简单技术。关于视网膜生理学的经典研究揭示了位于视网膜外丛状层和内丛状层的双极细胞或神经节细胞感受野的对比敏感度特性。为了解释这些观察结果，有人提出了高斯差分（DOG）滤波器，随后为了便于处理二维视网膜输入的计算而将其修改为高斯拉普拉斯（LOG）滤波器。迄今为止，科学和工程各分支中使用的几乎所有图像处理算法都遵循LOG或其变体之一。然而，视网膜生理学的最新观察表明，视网膜神经节细胞接收的输入来自比经典感受野更大的区域。我们根据这些最新观察结果，通过引入仅表示为高斯线性组合的高斯高阶导数，为视网膜神经节细胞的非经典感受野提出了一个各向同性模型。在数字图像处理中，这一方面提供了一种新的边缘检测机制，另一方面提供了图像半色调处理机制。还发现，生物系统有时可能倾向于在预处理阶段向接收到的信号中添加噪声来“感知”外部场景，以便在边缘图中获得关于明暗的更好信息。所提出的模型还为许多迄今为止不仅经典各向同性模型，而且其他格式塔和建构主义模型或非各向同性多尺度模型都无法解释的亮度对比错觉提供了解释。所提出的模型在模拟和数字领域都易于实现。一种模拟领域的实现方案生成了一个在硬件开发平台上实现的新硅视网膜模型。
