An Effective Surround Filter for Image Dehazing

Nair, Deepa; Kumar, Pattem Ashok; Sankaran, Praveen

doi:10.1145/2660859.2660926

Cited by 8 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another line of researches [29], [30] tries to make use of Retinex theory to approximate the spectral properties of object surfaces by the ratio of the reflected light. Very recently, [20] presented a re-formulation of (2) to integrate t (x) and A into one new variable.…”

Section: B Existing Methodology: An Overviewmentioning

confidence: 99%

Benchmarking Single Image Dehazing and Beyond

Ren

et al. 2017

Preprint

View full text Add to dashboard Cite

We present a comprehensive study and evaluation of existing single image dehazing algorithms, using a new largescale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics, to no-reference metrics, to subjective evaluation and the novel task-driven evaluation. Experiments on RESIDE shed light on the comparisons and limitations of state-of-the-art dehazing algorithms, and suggest promising future directions.

show abstract

Section: B Existing Methodology: An Overviewmentioning

confidence: 99%

Benchmarking Single Image Dehazing and Beyond

Ren

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…locally constant constraints and decorrelation of the transmission [87], dark channel prior [9], color attenuation prior [88], nonlocal prior [89]. In [90,91], Retinex theory is utilized to approximate the spectral properties of object surfaces by the ratio of the reflected light. Recently, Convolutional Neural Network (CNN)-based methods bring in the new prosperity for dehazing.…”

Section: B Poor Visibility Enhancementmentioning

confidence: 99%

Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study

Yang

Yuan

Ren

et al. 2020

IEEE Trans. on Image Process.

191

View full text Add to dashboard Cite

Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG 2+ challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions.Index Terms-Poor visibility environment, object detection, face detection, haze, rain, low-light conditions *The first two authors Wenhan Yang and Ye Yuan contributed equally. Ye Yuan and Wenhan Yang helped prepare the dataset proposed for the UG2+ Challenges, and were the main responsible members for UG2+ Challenge 2019 (Track 2) platform setup and technical support. Wenqi Ren, Jiaying Liu, Walter J. Scheirer, and Zhangyang Wang were the main organizers of the challenge and helped prepare the dataset, raise sponsors, set up evaluation environment, and improve the technical submission. Other authors are the group members of winner teams in UG2+ challenge Track 2 contributing to the winning methods.

show abstract

“…The enhancement-based hazy image processing method is based on directly obtaining the by-product of radiance scene recovery through visibility restoration by contrast enhancement/maximization. The algorithms in this category utilize contrast limited adaptive histogram equalization (CLAHE), histogram specification (HS) [47], and Retinex [48][49][50]. Additionally, some of these algorithms combine dark channel priors and transmission map extraction with contrast enhancement for refinement.…”

Section: Introductionmentioning

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

An Effective Surround Filter for Image Dehazing

Cited by 8 publications

References 18 publications

Benchmarking Single Image Dehazing and Beyond

Benchmarking Single Image Dehazing and Beyond

Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study

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

Contact Info

Product

Resources

About