An Enhanced Depth Approximation Model for Haze Removal Using Single Image

2020 ◽  
pp. 679-692
Author(s):  
Suresh Raikwar ◽  
Shashikala Tapaswi
Keyword(s):  
Approximation Model ◽  
Single Image ◽  
Haze Removal

scholarly journals Single image haze removal considering sensor blur and noise

2013 ◽  
Vol 2013 (1) ◽  
Author(s):  
Xia Lan ◽  
Liangpei Zhang ◽  
Huanfeng Shen ◽  
Qiangqiang Yuan ◽  
Huifang Li
Keyword(s):  
Single Image ◽  
Haze Removal

scholarly journals Single image haze removal using variable fog-weight

2020 ◽  
Vol 1706 ◽  
pp. 012091
Author(s):  
Mohammed Shoaib ◽  
Mohd Mohsin ◽  
Imbeshat Khalid Ansari ◽  
Harshat Maddhesiya ◽  
Upendra Kumar Acharya
Keyword(s):  
Single Image ◽  
Haze Removal

Single image fog and haze removal based on self-adaptive guided image filter and color channel information of sky region

2017 ◽  
Vol 77 (11) ◽  
pp. 13513-13530 ◽  
Author(s):  
Bo Jiang ◽  
Hongqi Meng ◽  
Jian Zhao ◽  
Xiaolei Ma ◽  
Siyu Jiang ◽  
...  
Keyword(s):  
Single Image ◽  
Color Channel ◽  
Image Filter ◽  
Haze Removal ◽  
Guided Image Filter ◽  
Channel Information ◽  
Self Adaptive

A Weighted Multi-scale Fusion Technique for Single Image Haze Removal

2021 ◽  
pp. 469-472
Author(s):  
Avishek Kumar ◽  
Rajib Kumar Jha ◽  
Naveen K. Nishchal
Keyword(s):  
Single Image ◽  
Fusion Technique ◽  
Haze Removal ◽  
Multi Scale

Cardinal color fusion network for single image haze removal

2019 ◽  
Vol 30 (2) ◽  
pp. 231-242 ◽  
Author(s):  
Akshay Dudhane ◽  
Subrahmanyam Murala
Keyword(s):  
Single Image ◽  
Haze Removal ◽  
Color Fusion

scholarly journals Transmission Estimation by Complex Assumption with Occlusion Handling

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Jiantao Liu ◽  
Xiaoxiang Yang ◽  
Mingzhu Zhu ◽  
Bingwei He
Keyword(s):  
Optimal Solution ◽  
Image Sensor ◽  
Natural Images ◽  
Single Image ◽  
Occlusion Handling ◽  
Image Dehazing ◽  
Haze Removal ◽  
Dark Channel ◽  
Transmission Estimation ◽  
Single Image Dehazing

Transmission estimation is a critical step in single-image dehazing. The estimate of each pixel describes the portion of the scene radiance that is degraded by hazing and finally reaches the image sensor. Transmission estimation is an underconstrained problem, and, thus, various assumptions, priors, and models are employed to make it solvable. However, most of the previous methods did not consider the different assumptions simultaneously, which, therefore, did not correctly reflect the previous assumptions in the final result. This paper focuses on this problem and proposes a method using an energy function that clearly defines the optimal transmission map and combines the assumptions from three aspects: fidelity, smoothness, and occlusion handling, simultaneously. Fidelity is measured by a novel principle derived from the dark channel prior, smoothness is described by the assumption of piecewise smoothening, and occlusion handling is achieved based on a new proposed feature. The transmissions are estimated by searching for the optimal solution of the function that can retain all the employed assumptions simultaneously. The proposed method is evaluated on the synthetic images of two datasets and various natural images. The results show that there is remarkable fidelity and smoothness in the transmission and that a good performance is exhibited for haze removal.

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