Adaptive sky detection and preservation in dehazing algorithm

Dai, Shi‐Lu; Tarel, Jean-Philippe

doi:10.1109/ispacs.2015.7432849

Cited by 9 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[64] consisting of left and right views of 66 synthetic images was also tested. The algorithms include Tarel and Hautiere[65], Dai et al[66], Nishino et al[67], He et al[32], Galdran et al[41], Wang and He[68], Zhu et al[69], Ren et al[46], partial differential equation-based single scale Retinex GOC-CLAHE (PDE-GOC-SSR-CLAHE)[52], PDE-IRCES[53], and PA. The proposed approach (PA) is much more vivid as it enhances edges and avoids discolouration of sky regions as seen in the Tiananmen image inFig.…”

mentioning

confidence: 99%

Adaptive Multi-Scale Entropy Fusion De-hazing based on Fractional Order

Nnolim¹

2018

Preprint

View full text Add to dashboard Cite

This paper describes a proposed fractional filter-based multi-scale underwater and hazy image enhancement algorithm. The proposed system combines a modified global contrast operator with fractional order-based multi-scale filters used to generate several images, which are fused based on entropy and standard deviation. The multi-scale-global enhancement technique enables fully adaptive and controlled colour correction and contrast enhancement without over exposure of highlights when processing hazy and underwater images. This in addition to illumination/reflectance estimation coupled with global and local contrast enhancement. The proposed algorithm is also compared with the most recent available state-of-the-art multi-scale fusion de-hazing algorithm. Experimental comparisons indicate that the proposed approach yields better edge and contrast enhancement results without halo effect, colour degradation and is faster and more adaptive than all other algorithms from the literature.

show abstract

mentioning

confidence: 99%

Adaptive Multi-Scale Entropy Fusion De-hazing based on Fractional Order

Nnolim¹

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition, the FRIDA3 dataset [92,93] consisting of left and right views of 66 synthetic images was also tested. The algorithms include Tarel and Hautiere [94], Dai et al [95], Nishino et al [96], He et al [32], Galdran et al [41], Wang and He [97], Zhu et al [91], Ren et al [46], Ju et al [98], partial differential equation-based single scale Retinex GOC-CLAHE (PDE-GOC-SSR-CLAHE or PDE-Retinex) [52], PDE-IRCES [53], and PA. All test images (apart from FRIDA3) used were obtained from the University of Texas at Austin Laboratory for Image & Video Engineering (LIVE) dataset, which can be downloaded from the website: http: //live.ece.utexas.edu/research/fog/fade_defade.html) [78]. More detailed and extensive results can be found in additional material [99,100].…”

mentioning

confidence: 99%

Adaptive Multi-Scale Entropy Fusion De-Hazing Based on Fractional Order

Nnolim¹

2018

J. Imaging

View full text Add to dashboard Cite

This paper describes a proposed fractional filter-based multi-scale underwater and hazy image enhancement algorithm. The proposed system combines a modified global contrast operator with fractional order-based multi-scale filters used to generate several images, which are fused based on entropy and standard deviation. The multi-scale-global enhancement technique enables fully adaptive and controlled color correction and contrast enhancement without over exposure of highlights when processing hazy and underwater images. This in addition to the illumination/reflectance estimation coupled with global and local contrast enhancement. The proposed algorithm is also compared with the most recent available state-of-the-art multi-scale fusion de-hazing algorithm. Experimental comparisons indicate that the proposed approach yields a better edge and contrast enhancement results without a halo effect, without color degradation, and is faster and more adaptive than all other algorithms from the literature.

show abstract