Low-Light Image Restoration Based on Retina Model using Neural Networks

10/04/2022
by   Yurui Ming, et al.
0

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.

READ FULL TEXT
research
05/03/2023

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

In this study, we explore the potential of using a straightforward neura...
research
12/12/2016

Autoencoder-based holographic image restoration

We propose a holographic image restoration method using an autoencoder, ...
research
10/04/2021

Max and Coincidence Neurons in Neural Networks

Network design has been a central topic in machine learning. Large amoun...
research
08/06/2019

Refining the Structure of Neural Networks Using Matrix Conditioning

Deep learning models have proven to be exceptionally useful in performin...
research
11/21/2018

Multivariate Forecasting of Crude Oil Spot Prices using Neural Networks

Crude oil is a major component in most advanced economies of the world. ...
research
06/29/2017

Music Signal Processing Using Vector Product Neural Networks

We propose a novel neural network model for music signal processing usin...
research
10/28/2021

Deep Calibration of Interest Rates Model

For any financial institution it is a necessity to be able to apprehend ...

Please sign up or login with your details

Forgot password? Click here to reset