在不同的颜色空间进行曲线映射的实验,证明在RGB空间进行映射可以获得最好的增强效果,而在其它的颜色空间进行映射会导致过饱和或者是色偏等质量退化问题。 在训练数据对模型性能影响的实验中发现,采用更多的训练数据,尤其是训练数据中同时包含光照图像和过曝光图像的时候,我们的方法可以取得最佳的一个增强表现。 作者还对...
2023 Elsevier Inc.In the case of insufficient illumination conditions, the quality of the captured image is poor. At this time, increasing the brightness of the dark region of the whole image will inevitably aggravate noise pollution. Therefore, the ideal state of low-light image enhancement shoul...
ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image Enhancement 格式:PDF 页数:11 上传日期:2024-11-08 02:08:40 浏览次数:2 下载积分:199 加入阅读清单 还剩10 页未读,是否继续阅读? 此文档由 leo_wyomin.. 分享于 2024-11-08...
Recently, deep-learning-based low-light image enhancement (LLIE) methods have made great progress. Benefiting from elaborately designed model architectures, these methods enjoy considerable performance gains. However, the generalizability of these methods may be weak, and they ...
Understand the concepts of custom loss functions and metrics for evaluating model performance in image enhancement tasks. Gain practical experience in training, evaluating, and fine-tuning deep learning models for low-light image enhancement using real-world datasets. ...
This repository is an implementation of [LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement] (https://arxiv.org/pdf/1511.03995.pdf) on Theano. It includes the codes and modules used for running LLNet via a Graphical User Interface. Users can choose to train the network...
Besides, we are the first, to the best of our knowledge, to compare the performance of deep learning-based low-light image enhancement methods on this kind of data. 3) We provide an online platform that covers many popular deep learning-based low-light image enhancement methods, where the ...
Single Low-light Image Enhancement Based on Multi-Scale Fusion and Deep Learning 作者:龙长念 来源:电子与通信工程, Master, 华中师范大学, 2020. DOI:10.27159/d.cnki.ghzsu.2020.003423 低光照 多尺度曝光 深度学习 混合学习 图像增强摘要 由于采集环境的影响,例如,在阴天,夜晚以及物体被遮挡等低照度条件下...
论文题目:Comparing deep learning models for low-light natural scene image enhancement and their impact on object detection and classification: Overview, empirical evaluation, and challenges 发表时间:28 August 2022 作者:RayanAl Sobbahi,JoeTekli
Deep Retinex Decomposition for Low-Light Enhancement 神经网络 Retinex模型是微光图像增强的有效工具。假设观测图像可以分解为反射率和光照。大多数现有的基于retinx的方法都为这种高度病态分解精心设计了手工制作的约束条件和参数,当应用于各种场景时,可能会受到模型容量的限制。在本文中,我们收集了一个包含低/正常光图...