Smoothing and enhancement algorithms for underwater images based on partial differential equations

Nnolim, Uche A.

doi:10.1117/1.jei.26.2.023009

Cited by 18 publications

(23 citation statements)

References 45 publications

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“…For the case of underwater image enhancement, we utilized a modified function known as gain offset correction contrast stretching (GOC-CS) [10] to perform colour correction for the effect of water prior to enhancement. This configuration was compared with PDE-based formulations utilizing GOC1, GOC2, piecewise linear transform (PWL) and histogram specification (HS) [15].…”

Section: Underwater Image Enhancementmentioning

confidence: 99%

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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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Section: Underwater Image Enhancementmentioning

confidence: 99%

“…Additionally, PDE-based enhancement-based approaches have been applied to de-hazing and underwater image processing [15] [16] [17] [18]. Furthermore, based on previous works, it was shown that the phenomenon of illumination was similar to haze [17].…”

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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“…Previously, extensive experiments where conducted (to determine effectiveness) on several contrast stretching algorithms [26]. Due to adjustable nature of the high and low values by adjusting the percentiles, the contrast stretching (CS) algorithm appeared to be much more versatile than the other algorithms.…”

Section: Selection and Modification Of Global Contrast Operatormentioning

confidence: 99%

“…Conversely some of the other algorithms were too harsh, had no effect or minimal impact on most underwater images, while others resulted in colour bleeding. The selected algorithms such as the piecewise linear transform (PWL) [56] and the gain offset correction (GOC) [57] were selected for incorporation into effective PDE -based formulations [26] [27]. This was because some underwater images responded better to GOC2 (due to its mainly colour correction ability) than to PWL (due to its generality) and vice versa.…”

Section: Selection and Modification Of Global Contrast Operatormentioning

confidence: 99%

“…Underwater image enhancement usually involves some colour correction/white balancing in addition to contrast enhancement process, which is usually a local/global operation. The GOC2 algorithm adequately processed underwater images, which required mild colour correction and contrast enhancement and thus avoided overexposure of highlights unlike most other tested contrast enhancement algorithms [26]. This necessitated the incorporation of a local contrast operator such as the CLAHE, which though effective, further added to the computational complexity of the algorithms and introduced additional parameters.…”

Section: Selection and Modification Of Global Contrast Operatormentioning

confidence: 99%

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

Nnolim¹

2018

Preprint

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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.

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A Comprehensive Review of Computational Dehazing Techniques

Singh

Kumar

2018

Arch Computat Methods Eng

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Smoothing and enhancement algorithms for underwater images based on partial differential equations

Cited by 18 publications

References 45 publications

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

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

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

A Comprehensive Review of Computational Dehazing Techniques

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