Improved partial differential equation‐based enhancement for underwater images using local–global contrast operators and fuzzy homomorphic processes

Nnolim, Uche A.

doi:10.1049/iet-ipr.2017.0259

Cited by 31 publications

(15 citation statements)

References 37 publications

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“…The representative images of these two groups include those unaffected by wide ranges while the other exhibits over-exposure for narrow ranges. This was partly the reason that the PWL approach was utilized in previous work [27].…”

Section: Preliminary Resultsmentioning

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%

“…The respective entropies ( , ) and standard deviations ( , ) of the aggregated images are computed (in eqns. (26) and (27)) and used to decide the best image outcome, ( , ) in (28), which is then processed with the modified global contrast enhancement algorithm to obtain the final output image, ( , ) in (29). This is based on the simultaneous multi-level high frequency component (edges and details) enhancement and multi-level low frequency component attenuation.…”

Section: Multi-scale Illumination/reflectance Contrast Enhancement (Mmentioning

confidence: 99%

“…Results are presented in Fig. 4, which contains results from [5], amended with results from [27] and PA and show that there is a considerable contrast and edge enhancement as details are seen much more clearly with minimal haze. For the fish image in Fig.…”

Section: Underwater Imagesmentioning

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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Section: Preliminary Resultsmentioning

confidence: 99%

Section: Selection and Modification Of Global Contrast Operatormentioning

confidence: 99%

Section: Multi-scale Illumination/reflectance Contrast Enhancement (Mmentioning

confidence: 99%

Section: Underwater Imagesmentioning

confidence: 99%

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

Nnolim¹

2018

Preprint

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“…Because of these advantages, variant LB methods have been proposed as an alternative to the conventional numerical solvers of PDEs in CFD, e.g., diffusion equations, convection diffusion equations, reaction diffusion equations, etc. Inspired by the application of PDEs in image processing, LB models have been applied as a novel method to the field of PDE-based image processing [8] in the past decade, e.g., image segmentation [9], image denoising [10] and image enhancement [11] by elaborating specific LB models.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Image Registration Algorithm Based on a Lattice Boltzmann Model

Chen

Courbebaisse

2019

Information

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Image registration is a key pre-procedure for high level image processing. However, taking into consideration the complexity and accuracy of the algorithm, the image registration algorithm always has high time complexity. To speed up the registration algorithm, parallel computation is a relevant strategy. Parallelizing the algorithm by implementing Lattice Boltzmann method (LBM) seems a good candidate. In consequence, this paper proposes a novel parallel LBM based model (LB model) for image registration. The main idea of our method consists in simulating the convection diffusion equation through a LB model with an ad hoc collision term. By applying our method on computed tomography angiography images (CTA images), Magnet Resonance images (MR images), natural scene image and artificial images, our model proves to be faster than classical methods and achieves accurate registration. In the continuity of 2D image registration model, the LB model is extended to 3D volume registration providing excellent results in domain such as medical imaging. Our method can run on massively parallel architectures, ranging from embedded field programmable gate arrays (FPGAs) and digital signal processors (DSPs) up to graphics processing units (GPUs).

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Improved Intrinsic Image Decomposition Technique for Image Contrast Enhancement Using Back Propagation Algorithm

Kour

Kaur

2019

Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB)

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Improved partial differential equation‐based enhancement for underwater images using local–global contrast operators and fuzzy homomorphic processes

Cited by 31 publications

References 37 publications

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

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

A Parallel Image Registration Algorithm Based on a Lattice Boltzmann Model

Improved Intrinsic Image Decomposition Technique for Image Contrast Enhancement Using Back Propagation Algorithm

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