Image Compression Regeneration and Depth Extraction by Silicon Retina

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This report describes the use of Cellular Neural Networks (CNNs) for information coding and decoding-especially for the case of moving images. The dynamics of the coding (C-) and decoding (D-) CNNs are described by generalized CNN state equations. The C-CNN encodes the image by resolution compression and halftoning. The D-CNN decodes the received data through a reconstruction process so as to almost recognize the original input to the C-CNN.A dynamic quantization is performed in the C-CNN to decide the binary value of each pixel from the neighboring values. In order to reduce the error between the original gray image and reconstructed halftone image, the template synthesis problem is addressed from the viewpoint of energy minimization. The resolution compression template synthesis problem is discussed from the viewpoints of topological and regularization theories. The structurally compressed image is regenerated in the D-CNN by a dynamic current distribution.The communication system in which the C- and D-CNNs are embedded consists of a differential transmitter with an internal receiver model in the feedback loop.Also, this report describes dynamic depth extraction for binocular stereo visual information by CNN.The correspondence problem can be solved by pattern recognition for analog images reconstructed from the transmitted funneling halftoning images. The competitive CNN is used.Finnaly, this paper describes resolutionable cellular neural network by which a wide range of image applications can be done based on spatio-temporal dynamics to generate triplet [Red Green Blue (RGB)] combinational secondary color for a full color input with any resolution. Area intensity of the secondary color can be generated by using local dynamics of inner cells in each pixel and color image processing can be done by using global CNN dynamics for the secondary color outputs and full color inputs.