不规则采样插值网络的学习:一些收敛性质

Applied Vision Pub Date : 1900-01-01 DOI:10.1364/av.1989.wc3

A. Ahumada, J. Mulligan

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引用次数: 1

摘要

最近，Ahumada和Yellott(1)以及Maloney(5,6)提出了一种训练网络的方案，用于重建不规则采样的视网膜图像。在这些方案中，可调加权网络补偿了视网膜阵列的不规则性和中间路径的几何畸变。本文提出了一些关于训练算法收敛性的思想。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Learning in Interpolation Networks for Irregular Sampling: Some Convergence Properties

Recently, Ahumada and Yellott (1) and Maloney (5,6) have presented schemes for training networks designed to reconstruct irregularly sampled retinal images. In these schemes adjustable weighting networks provide compensation for the irregularities in the retinal array and the geometrical distortions in intermediate pathways. This paper presents some ideas relating to the convergence of the training algorithms.

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Applied Vision

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