Denight: nighttime image enhancement using daytime image

2008 ◽  
Author(s):  
Akito Yamasaki ◽  
Hidenori Takauji ◽  
Shun'ichi Kaneko ◽  
Takeo Kanade ◽  
Hidehiro Ohki
Keyword(s):  
Image Enhancement ◽  
Nighttime Image

Nighttime image enhancement based on image decomposition

2018 ◽  
Vol 13 (1) ◽  
pp. 189-197 ◽  
Author(s):  
Xuesong Jiang ◽  
Hongxun Yao ◽  
Dilin Liu
Keyword(s):  
Image Enhancement ◽  
Image Decomposition ◽  
Nighttime Image

scholarly journals A nighttime image enhancement method based on Retinex and guided filter for object recognition of apple harvesting robot

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141775387 ◽  
Author(s):  
Ji Wei ◽  
Qian Zhijie ◽  
Xu Bo ◽  
Zhao Dean
Keyword(s):  
Image Enhancement ◽  
Bilateral Filter ◽  
Histogram Equalization ◽  
Guided Filter ◽  
Edge Preserving ◽  
Performance Indexes ◽  
Retinex Algorithm ◽  
Enhancement Method ◽  
Harvesting Robot ◽  
Nighttime Image

In order to improve the working efficiency of robot promptly picking ripe apples, the harvesting robot must have the ability of continuous recognition and operation at night. Nighttime apple image has many dark spaces and shadows with low resolution, and therefore, a Retinex algorithm based on guided filter is presented to enhance nighttime image in this article. According to color feature of image, the illumination component is estimated by using guided filter which can be applied as an edge-preserving smoothing operator. And the reflection component with image itself characteristics is obtained by employing single-scale Retinex algorithm. After gamma correction, these two components of image are synthesized into a new enhanced nighttime apple image. Fifty nighttime images acquired under fluorescent lighting are selected to make experiment. Experimental results show that the image enhancement performance indexes processed by the proposed algorithm, such as average gray value, standard deviation, information entropy, average gradient, and segmentation error are superior to those of histogram equalization algorithms and Retinex algorithm based on bilateral filter. In addition, compared with the Retinex algorithm based on bilateral filter, the proposed algorithm has an average reduction of 74.56% in running time with better real-time and higher efficiency. So it can realize the continuous operation of apple harvesting robot at night.

Denight: Nighttime Image Enhancement Using Daytime Image

2009 ◽  
Vol 129 (12) ◽  
pp. 2222-2231
Author(s):  
Akito Yamasaki ◽  
Takeo Kanade ◽  
Hidenori Takauji ◽  
Hidehiro Ohki ◽  
Shun'ichi Kaneko
Keyword(s):  
Image Enhancement ◽  
Nighttime Image

Multi-scale retinex improvement for nighttime image enhancement

Optik ◽  
2014 ◽  
Vol 125 (24) ◽  
pp. 7143-7148 ◽  
Author(s):  
Haoning Lin ◽  
Zhenwei Shi
Keyword(s):  
Image Enhancement ◽  
Multi Scale ◽  
Nighttime Image

Parameter‐adaptive nighttime image enhancement with multi‐scale decomposition

2016 ◽  
Vol 10 (5) ◽  
pp. 425-432 ◽  
Author(s):  
Shuhang Wang ◽  
Jin Zheng ◽  
Bo Li
Keyword(s):  
Image Enhancement ◽  
Multi Scale ◽  
Nighttime Image ◽  
Parameter Adaptive

Nighttime image enhancement using a new illumination boost algorithm

2019 ◽  
Vol 13 (8) ◽  
pp. 1314-1320 ◽  
Author(s):  
Zohair Al-Ameen
Keyword(s):  
Image Enhancement ◽  
Nighttime Image

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.

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