In this study, we explore the potential of using a straightforward neural
network inspired by the retina model to efficiently restore low-light images.
The retina model imitates the neurophysiological principles and dynamics of
various optical neurons. Our proposed neural network model reduces the
computational overhead compared to traditional signal-processing models while
achieving results similar to complex deep learning models from a subjective
perceptual perspective. By directly simulating retinal neuron functionalities
with neural networks, we not only avoid manual parameter optimization but also
lay the groundwork for constructing artificial versions of specific
neurobiological organizations.

本文中，我们提出了一种受视网膜模型启发的神经网络，可以高效地恢复低光图像，并通过直接模拟视网膜神经元的功能来构建特定的生物神经学组织的人工版本。从主观感知的角度来看，我们的模型达到了与复杂的深度学习模型相似的效果，并且相对于传统的信号处理模型减少了计算开销。

低光图像恢复的仿生简单神经网络：一种极简主义方法

Bio-Inspired Simple Neural Network for Low-Light Image Restoration: A  Minimalist Approach

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.

本研究探究了使用类脑神经网络模拟人类视网膜模型的可能性，旨在通过模仿各种光学神经元的神经生理原理和动态学，使用简单的神经网络实现低光照图片的无缝恢复。该模型可以节省计算成本，并从主观感知角度产生与复杂深度学习模型相媲美的结果，同时避免了手动寻找最佳参数的过程，为构建相应的神经生物组织的人工版本铺平了道路。