Bio-Inspired Simple Neural Network for Low-Light Image Restoration: A Minimalist Approach
This work addresses low-light image restoration for applications like photography or vision systems, but it is incremental as it builds on existing bio-inspired and neural network approaches.
The paper tackled low-light image restoration using a simple neural network inspired by the retina model, achieving results similar to complex deep learning models with reduced computational overhead.
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.