A Comprehensive Review of Computational Dehazing Techniques

Singh, Dilbag; Kumar, Vijay

doi:10.1007/s11831-018-9294-z

Cited by 83 publications

(46 citation statements)

References 150 publications

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“…Optimization algorithms outperform other methods by obtaining visually compelling results. Recently, Singh [16] has reviewed dehazing algorithm explaining various techniques adopted by researchers in this field.…”

Section: Related Workmentioning

confidence: 99%

“…The personal best position of i th particle is obtained by finding the minimum fog density "D" as follows: (15) For every iteration, the personal and global best values are updated so that the optimal value is chosen when fitness function "D" becomes minimum. Also, the velocity v(t) and particle position x(t) are updated using equation (16) and (17) until the iteration converges. (17) where w is inertia, c1 and c2 are accelerating coefficients, r1 and r2 are random values ranging between 0 and 1.…”

Section: A Implementation Of Pso For Dehazingmentioning

confidence: 99%

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Atmospheric Light Estimation using Particle Swarm Optimization for Dehazing

Padmini¹,

Shankar²

2019

IJACSA

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For the past decade, many researchers have been working towards the improvement in the visibility of single hazy images, using the haze image model. According to the haze image model, the hazy-free image is restored by estimating the atmospheric light and transmission from a hazy image. The objective of this proposed work is to improve the perceptibility by decreasing the density of haze in the hazy images. The research work was carried to estimate the optimal value of atmospheric light by tuning the weights using a bioinspired technique called Particle Swarm Optimization (PSO) based on the objective of minimizing the fog density. We have selected a fitness function or objective function which incorporates all statistical features to differentiate a clear image from the hazy image. The results are validated with the state-of-the-art, by measuring fog density of the restored image using Fog Aware Density Evaluator (FADE). Also, the results are validated by measuring the Peak signal to noise ratio (PSNR) and structural similarity index (SSI) using ground truth images from Foggy Road image database (FRIDA). This research work demonstrates better results qualitatively and quantitatively.

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Section: Related Workmentioning

confidence: 99%

Section: A Implementation Of Pso For Dehazingmentioning

confidence: 99%

Atmospheric Light Estimation using Particle Swarm Optimization for Dehazing

Padmini¹,

Shankar²

2019

IJACSA

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“…It is difficult to guarantee high-quality dehazing results under some imaging conditions. Please refer to recent reviews [1,24] for more progresses on image dehazing.…”

Section: Introductionmentioning

confidence: 99%

Variational Regularized Transmission Refinement for Image Dehazing

Shu

Xiao

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

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High-quality dehazing performance is highly dependent upon the accurate estimation of transmission map. In this work, the coarse estimation version is first obtained by weightedly fusing two different transmission maps, which are generated from foreground and sky regions, respectively. A hybrid variational model with promoted regularization terms is then proposed to assisting in refining transmission map. The resulting complicated optimization problem is effectively solved via an alternating direction algorithm. The final haze-free image can be effectively obtained according to the refined transmission map and atmospheric scattering model. Our dehazing framework has the capacity of preserving important image details while suppressing undesirable artifacts, even for hazy images with large sky regions. Experiments on both synthetic and realistic images have illustrated that the proposed method is competitive with or even outperforms the state-of-the-art dehazing techniques under different imaging conditions.

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“…Thus, there is the need for haze removal to improve image visibility and contrast in computer vision applications such as surveillance, intelligent transport systems, etc. Image de-hazing is a current and active area of research in image processing [2]. There are numerous approaches which are classified as single-or multi-image-based schemes [2].…”

Section: Introductionmentioning

confidence: 99%

“…Image de-hazing is a current and active area of research in image processing [2]. There are numerous approaches which are classified as single-or multi-image-based schemes [2]. Furthermore, interest has shifted to single image based methods due to feasibility and cost concerns inherent in multiple image-based methods.…”

Section: Introductionmentioning

confidence: 99%

Improved partial differential equation and fast approximation algorithm for hazy/underwater/dust storm image enhancement

Nnolim¹

2019

AIVP

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This paper presents an improved and modified partial differential equation (PDE)-based de-hazing algorithm. The proposed method combines logarithmic image processing models in a PDE formulation refined with linear filter-based operators in either spatial or frequency domain. Additionally, a fast, simplified de-hazing function approximation of the hazy image formation model is developed in combination with fuzzy homomorphic refinement. The proposed algorithm solves the problem of image darkening and over-enhancement of edges in addition to enhancement of dark image regions encountered in previous formulations. This is in addition to avoiding enhancement of sky regions in dehazed images while avoiding halo effect. Furthermore, the proposed algorithm is utilized for underwater and dust storm image enhancement with the incorporation of a modified global contrast enhancement algorithm. Experimental comparisons indicate that the proposed approach surpasses a majority of the algorithms from the literature based on quantitative image quality metrics.Keywords: Logarithmic image processing; illumination-reflectance model; filter kernel-based enhancement; filter-based dynamic range compression; power law-based illumination correction; partial differential equations. A d v a n c e s i n I m a g e a n d V i d e o P r o c e s s i n g ; V o l u m e 7 , N o . 3 , J u n e 2 0 1 9 S o c i e t y f o r S c i e n c e a n d E d u c a t i o n , U n i t e d K i n g d o m 13the Laplacian-based soft mapping procedure in the original DCP, He et al proposed the guided filter to reduce the runtime [5]. However, the guided filter does not preserve fine structures according to Li and Zheng [6]. Thus, they proposed a novel globally guided image filtering (G-GIF) composed of both global structure transfer filter and global edge-preserving smoothing filters for image fine structure preservation-based de-hazing [6].Recently, fusion [7] [8] [9] [10] [11] and deep learning-based approaches [12] have become increasingly popular and widespread [2] due to faster and greater computing resources. However, a vast amount of images is required in the training stage in addition to considerable computing resources and runtime for the deep learning approaches. The fusion-based methods are relatively much more involved than the purely enhancement-based approaches but effective in most cases. Also, the restoration methods require tuning of several parameters for different images to obtain best results. Brief overview and backgroundThe haze in images can be likened to illumination in dark or shadowy images [17] since the end result of uneven illumination and haze is the reduction in image visibility and contrast. Thus, by maximizing contrast, the visibility of the image scene is enhanced, reducing the haze or uneven illumination. This Uche A. Nnolim; Improved Partial Differential Equation and Fast Approximation Algorithm for Hazy/Underwater/Dust Storm Image Enhancement

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A Comprehensive Review of Computational Dehazing Techniques

Cited by 83 publications

References 150 publications

Atmospheric Light Estimation using Particle Swarm Optimization for Dehazing

Atmospheric Light Estimation using Particle Swarm Optimization for Dehazing

Variational Regularized Transmission Refinement for Image Dehazing

Improved partial differential equation and fast approximation algorithm for hazy/underwater/dust storm image enhancement

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