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图像光照增强是一种常见的图像处理技术，旨在改善图像的亮度和对比度，使其更加清晰和易于观察。在本文中，我们将介绍一种基于双边滤波和局部均值算法的图像光照增强方法，并提供相应的MATLAB代码实现。
双边滤波是一种常用的图像平滑技术，它可以在保持边缘信息的同时去除噪声。通过结合双边滤波和局部均值算法，我们可以实现图像光照增强。
下面是MATLAB代码示例：
function enhanced_image = enhance_image(input_image, sigma_spatial, sigma_range, window_size)
% 输入参数：
% input_image: 输入图像
% sigma_spatial: 空间域标准差使用双边滤波和局部均值算法进行图像光照增强
本文将介绍一种基于双边滤波和局部均值算法的图像光照增强方法，并提供相应的MATLAB代码。本文介绍了一种基于双边滤波和局部均值算法的图像光照增强方法，并给出了相应的MATLAB代码。局部均值算法是一种直观的光照增强方法，它的基本思想是将图像分成若干个小区域，计算每个区域内像素灰度值的均值作为该区域的光照值，然后根据这些光照值调整图像的亮度。可以看出，使用双边滤波和局部均值算法实现的光照增强方法，使得图片的亮度明显提高，同时细节信息得到了保留。双边滤波是一种非线性滤波算法，它可以保留图像细节的同时平滑噪声。
YOLOv8【第八章：特殊场景检测篇·第1节】一文搞懂，低光照图像检测增强技术！
🏆 本文收录于 《YOLOv8实战：从入门到深度优化》 专栏。该专栏系统复现并梳理全网各类 YOLOv8 改进与实战案例（当前已覆盖分类 / 检测 / 分割 / 追踪 / 关键点 / OBB 检测等方向），坚持持续更新 + 深度解析，质量分长期稳定在 97 分以上，可视为当前市面上 覆盖较全、更新较快、实战导向极强 的 YOLO 改进系列内容之一。
图像细节增强算法
基于卷积神经网络（CNN）的细节增强算法通常涉及使用深度学习技术，特别是卷积神经网络来学习图像的特征表示，以提高图像的质量和细节。它通过学习图像的映射来增强图像的细节，并且在一些情况下对细节有显著的改善。EDSR是用于超分辨率的一种改进型深度卷积神经网络，它专注于学习图像的高频信息，从而提高图像的细节。MemNet 是一种记忆网络，专注于记忆和学习图像的长程依赖关系，有助于保留和增强图像的细节。RCAN是一种专注于学习图像残差信息和通道注意力的网络结构，可以用于提高图像的细节和清晰度。
局部均值滤波(local statistics neibor filter)
局部均值滤波(local statistics neibor filter) 用于人脸美颜
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. 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汉书HE8，好用的汇编代码编写工具
汉书HE8，好用的汇编代码编写工具早期编写汇编代码所用功能强大
jdk-8u144-linux-x64.tar.gz.
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