Fusion Algorithm for Foggy Image Enhancement Based on Transmittance Weight Factor

2021 ◽  
Author(s):  
Wei Sun ◽  
Meng Zhang ◽  
Jintao Wang
Keyword(s):  
Image Enhancement ◽  
Weight Factor ◽  
Fusion Algorithm

A blurry low-light image enhancement and deblurring fusion algorithm

2018 ◽  
Author(s):  
Guannan Chen ◽  
Huijing Lin ◽  
Chao Wei ◽  
Aisheng Xu ◽  
Haotian Yu ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Fusion Algorithm ◽  
Low Light ◽  
Light Image

scholarly journals Fusion algorithm of visible and infrared image based on anisotropic diffusion and image enhancement (capitalize only the first word in a title (or heading), the first word in a subtitle (or subheading), and any proper nouns)

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0245563
Author(s):  
Hui Huang ◽  
Linlu Dong ◽  
Zhishuang Xue ◽  
Xiaofang Liu ◽  
Caijian Hua
Keyword(s):  
Image Enhancement ◽  
Anisotropic Diffusion ◽  
Infrared Image ◽  
Source Image ◽  
Laplacian Pyramid ◽  
Infrared Images ◽  
Fusion Algorithm ◽  
Public Data ◽  
Visible Images ◽  
Enhancement Algorithm

Aiming at the situation that the existing visible and infrared images fusion algorithms only focus on highlighting infrared targets and neglect the performance of image details, and cannot take into account the characteristics of infrared and visible images, this paper proposes an image enhancement fusion algorithm combining Karhunen-Loeve transform and Laplacian pyramid fusion. The detail layer of the source image is obtained by anisotropic diffusion to get more abundant texture information. The infrared images adopt adaptive histogram partition and brightness correction enhancement algorithm to highlight thermal radiation targets. A novel power function enhancement algorithm that simulates illumination is proposed for visible images to improve the contrast of visible images and facilitate human observation. In order to improve the fusion quality of images, the source image and the enhanced images are transformed by Karhunen-Loeve to form new visible and infrared images. Laplacian pyramid fusion is performed on the new visible and infrared images, and superimposed with the detail layer images to obtain the fusion result. Experimental results show that the method in this paper is superior to several representative image fusion algorithms in subjective visual effects on public data sets. In terms of objective evaluation, the fusion result performed well on the 8 evaluation indicators, and its own quality was high.

Image Enhancement Based on Selective - Retinex Fusion Algorithm

2012 ◽  
Vol 7 (6) ◽  
Author(s):  
Xuebo Jin ◽  
Jia Bao ◽  
Jingjing Du
Keyword(s):  
Image Enhancement ◽  
Fusion Algorithm

scholarly journals Contrast Limited Adaptive Histogram Equalization Based Fusion for Underwater Image Enhancement

2017 ◽  
Author(s):  
JINXIANG MA ◽  
Xinnan Fan ◽  
Simon X. Yang ◽  
Xuewu Zhang ◽  
Xifang Zhu
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Histogram Equalization ◽  
Color Spaces ◽  
Edge Detector ◽  
Fusion Algorithm ◽  
Adaptive Histogram Equalization ◽  
Underwater Image ◽  
Rgb Images ◽  
Three Components

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

Contrast Limited Adaptive Histogram Equalization-Based Fusion in YIQ and HSI Color Spaces for Underwater Image Enhancement

2018 ◽  
Vol 32 (07) ◽  
pp. 1854018 ◽  
Author(s):  
Jinxiang Ma ◽  
Xinnan Fan ◽  
Simon X. Yang ◽  
Xuewu Zhang ◽  
Xifang Zhu
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Histogram Equalization ◽  
Color Spaces ◽  
Fusion Algorithm ◽  
Noise Interference ◽  
Adaptive Histogram Equalization ◽  
Rgb Images ◽  
Color Cast ◽  
Three Components

To improve contrast and restore color for underwater images without suffering from insufficient details and color cast, this paper proposes a fusion algorithm for different color spaces based on contrast limited adaptive histogram equalization (CLAHE). The original color image is first converted from RGB space to two different spaces: YIQ and HSI. Then, the algorithm separately applies CLAHE in YIQ and HSI color spaces to obtain two different enhanced images. After that, the YIQ and HSI enhanced images are respectively converted back to RGB space. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, using a 4-direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse the YIQ-RGB and HSI-RGB images together to achieve the final image. The experimental results showed that the proposed algorithm provided more detail enhancement and higher values of color restoration than other image enhancement algorithms. The proposed algorithm can effectively reduce noise interference and observably improve the image quality for underwater images.

Exploring Coronal Structures with SOHO

2000 ◽  
Vol 179 ◽  
pp. 403-406
Author(s):  
M. Karovska ◽  
B. Wood ◽  
J. Chen ◽  
J. Cook ◽  
R. Howard
Keyword(s):  
Solar Corona ◽  
Image Enhancement ◽  
Coronal Mass Ejections ◽  
Dynamical Evolution ◽  
Small Scale ◽  
Low Contrast ◽  
Contrast Structures ◽  
Scale Structures ◽  
Small Scale Structures ◽  
Coronal Structures

AbstractWe applied advanced image enhancement techniques to explore in detail the characteristics of the small-scale structures and/or the low contrast structures in several Coronal Mass Ejections (CMEs) observed by SOHO. We highlight here the results from our studies of the morphology and dynamical evolution of CME structures in the solar corona using two instruments on board SOHO: LASCO and EIT.

Sign in / Sign up

Export Citation Format

Share Document