scholarly journals Image Fusion Based on Color Transfer Technique

10.5772/17413 ◽  
2011 ◽  
Author(s):  
Guangxin Li
Keyword(s):  
Image Fusion ◽  
Color Transfer

True color transfer for dual band image fusion

2014 ◽  
Vol 7 (3) ◽  
pp. 402-410
Author(s):  
许廷发 XU Ting-fa ◽  
李俊涛 LI Jun-tao ◽  
张一舟 ZHANG Yi-zhou ◽  
申子宜 SHEN Zi-yi ◽  
郭巳秋 GUO Si-qiu
Keyword(s):  
Image Fusion ◽  
Dual Band ◽  
Color Transfer ◽  
True Color ◽  
Band Image

scholarly journals Deep Color Transfer for Color-Plus-Mono Dual Cameras

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2743
Author(s):  
Hae Woong Jang ◽  
Yong Ju Jung
Keyword(s):  
Image Fusion ◽  
Stereo Matching ◽  
Color Image ◽  
Structural Information ◽  
Just Noticeable Difference ◽  
Color Transfer ◽  
Fused Image ◽  
Visual Artifacts ◽  
Transfer Method ◽  
Deep Learning Model

A few approaches have studied image fusion using color-plus-mono dual cameras to improve the image quality in low-light shooting. Among them, the color transfer approach, which transfers the color information of a color image to a mono image, is considered to be promising for obtaining improved images with less noise and more detail. However, the color transfer algorithms rely heavily on appropriate color hints from a given color image. Unreliable color hints caused by errors in stereo matching of a color-plus-mono image pair can generate various visual artifacts in the final fused image. This study proposes a novel color transfer method that seeks reliable color hints from a color image and colorizes a corresponding mono image with reliable color hints that are based on a deep learning model. Specifically, a color-hint-based mask generation algorithm is developed to obtain reliable color hints. It removes unreliable color pixels using a reliability map computed by the binocular just-noticeable-difference model. In addition, a deep colorization network that utilizes structural information is proposed for solving the color bleeding artifact problem. The experimental results demonstrate that the proposed method provides better results than the existing image fusion algorithms for dual cameras.

Infrared and Low-Light-Level Image Fusion Method Based on Sparse Representation and Color Transfer

2014 ◽  
Vol 511-512 ◽  
pp. 462-466
Author(s):  
Shi Hong Xu ◽  
Guo Qing Huang ◽  
Cun Chao Liu ◽  
Chun Ping Xiong
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Color Image ◽  
Light Level ◽  
Color Contrast ◽  
Fusion Method ◽  
Color Transfer ◽  
Low Light ◽  
Common Component ◽  
Natural Color

A natural color fusion method for infrared and low-light-level image is proposed. This method utilizes image fusion and color transfer. The fused image uses sparse representation to merge the source images information to be assigned to the Y channel. And then the I and Q channel is combined using Toets method, which extracts the common component from the source images. Finally, the false-color image is obtained by using color transfer technology to the prior pseudo-color YIQ image. Experiments show that the result of our method is information that is more salient, has a higher color contrast, and a more natural color appearance when compared with those produced by the traditional coloration algorithm.

Mid-wave Infrared Image Fusion in Subdivision Bands with Color Transfer

2013 ◽  
Vol 8 (8) ◽  
pp. 1004-1012
Author(s):  
Lin SuZhen ◽  
Yang FengBao ◽  
He Dong ◽  
Niu Tao
Keyword(s):  
Image Fusion ◽  
Infrared Image ◽  
Color Transfer

Polarization Image Fusion Method Using Color Transfer and Clustering-Based Segmentation

2011 ◽  
Vol 40 (1) ◽  
pp. 149-153 ◽  
Author(s):  
周浦城 ZHOU Pu-cheng ◽  
张洪坤 ZHANG Hong-kun ◽  
薛模根 XUE Mo-gen
Keyword(s):  
Image Fusion ◽  
Fusion Method ◽  
Color Transfer ◽  
Polarization Image ◽  
Image Fusion Method
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