.png){kind=icon}
返回
A Color Correction Method Based on Incremental Multilevel Iterative Histogram Matching
.png){kind=icon}
PRE
AI
DOI：10.1109/JSEN.2024.3432277.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The stitched images produced by multicamera systems often suffer from unnatural seams and poor color consistency, which seriously impact the accuracy of post-processing and degrade the viewing experience. The existing color correction solutions still have encountered issues such as inadequate correction accuracy and color loss. To solve these issues, we have proposed an incremental multilevel iterative histogram matching (IMIHM) color correction method. The proposed method effectively addresses color deviation by partitioning the image using an adjustable sliding window and performing incremental iterative matching at each level. Additionally, a weighted fusion algorithm is employed to ensure the generation of seamless and color-consistent images. This method has the advantages of both color consistency of global color correction and high accuracy of local color correction. Our proposed method has been successfully applied to a 5x5 camera array system, and experimental results demonstrate that it can eliminate color deviation significantly. Compared to the state-of-the-art hybrid histogram matching (HHM) algorithm, our method exhibits superior performances, with approximately 61.6% improvement in histogram similarity (HSM), 11.6% improvement in mean-squared error (MSE), and 4.2% improvement in peak signal-to-noise ratio (PSNR).
Keyword:
Color correction
global color consistency
histogram matching (HM)
incremental iterative
local color mapping
Color correction
global color consistency
histogram matching (HM)
incremental iterative
local color mapping
期刊
IF:
4.5
论文数:
2.2W
被引数:
7.3W
机构
引用论文
.png){kind=icon}
0.18um low voltage 12-bit successive-approximation-register analog-to-digital converter (SAR ADC)
PREAI
分享收藏Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweets
PREAI
分享收藏分享收藏RANDOM SAMPLE CONSENSUS - A PARADIGM FOR MODEL-FITTING WITH APPLICATIONS TO IMAGE-ANALYSIS AND AUTOMATED CARTOGRAPHY随机样本共识-模型拟合的范例，可应用于图像分析和自动制图
OAAI
OAAI分享收藏
查看更多内容