论文标题
部分可观测时空混沌系统的无模型预测
Low-Light Image Restoration Based on Retina Model using Neural Networks
论文作者
论文摘要
我们报告了使用简单的神经网络轻松恢复受视网膜模型启发的低光图像的可能性，该图像模仿了各种类型的光学神经元的神经生理学原理和动力学。与传统的信号处理模型相比，提出的神经网络模型可节省计算开销的成本，并从主观的感知角度产生与复杂的深度学习模型相当的结果。这项工作表明，使用神经网络直接模拟视网膜神经元的功能不仅避免了手动寻求最佳参数，而且还铺平了为某些神经生物学组织构建相应人工版本的方式。
We report the possibility of using a simple neural network for effortless restoration of low-light images inspired by the retina model, which mimics the neurophysiological principles and dynamics of various types of optical neurons. The proposed neural network model saves the cost of computational overhead in contrast with traditional signal-processing models, and generates results comparable with complicated deep learning models from the subjective perceptual perspective. This work shows that to directly simulate the functionalities of retinal neurons using neural networks not only avoids the manually seeking for the optimal parameters, but also paves the way to build corresponding artificial versions for certain neurobiological organizations.
