论文的核心思想类似于Retinex,使用了三个尺度的高斯模糊,再和原图做减法,获得不同程度的细节信息,然后通过一定的组合方式把这些细节信息融合到原图中,从而得到加强原图信息的能力。值得一提的就是对D1的系数做了特殊的处理,算法的编码简单、效果明显。 对应的python实现 # -*- coding: utf-8 -*- import cv2 imp...
的第2.3节 论文的核心思想类似于Retinex,使用了三个尺度的高斯模糊,再和原图做减法,获得不同程度的细节信息,然后通过一定的组合方式把这些细节信息融合到原图中,从而得到加强原图信息的能力。值得一提的就是对D1的系数做了特殊的处理,算法的编码简单、效果明显。 对应的python实现 # -*- coding: utf-8 -*- imp...
论文的核心思想类似于Retinex,使用了三个尺度的高斯模糊,再和原图做减法,获得不同程度的细节信息,然后通过一定的组合方式把这些细节信息融合到原图中,从而得到加强原图信息的能力。值得一提的就是对D1的系数做了特殊的处理,算法的编码简单、效果明显。 对应的python实现 # -*- coding: utf-8-*-import cv2 import...
[13] combined pixel distribution remapping with a multipriority Retinex variational model to address color shift and brightness loss, which efficiently improves image clarity. MCLA [14] utilizes multicolor model conversion and leverages background light and transmission maps, which achieves a good ...
[13] combined pixel distribution remapping with a multipriority Retinex variational model to address color shift and brightness loss, which efficiently improves image clarity. MCLA [14] utilizes multicolor model conversion and leverages background light and transmission maps, which achieves a good ...
More recently, zero-reference-based methods have proved highly efficient and cost-effective, and fewer images are needed, which has caused a stir in the fields of LLIE. For example, RRDNet [60] decomposed an image into illumination, reflectance, and noise, then the Retinex reconstruction loss,...
[13] combined pixel distribution remapping with a multipriority Retinex variational model to address color shift and brightness loss, which efficiently improves image clarity. MCLA [14] utilizes multicolor model conversion and leverages background light and transmission maps, which achieves a good ...