Performance Evaluation of Spatial Domain Contrast Enhancement Techniques for Underwater Images

Singh, Shivendra; Soni, Monika; Patel, Arun Kumar; Mishra, Ravi Shankar

doi:10.5120/16263-5936

Cited by 10 publications

(9 citation statements)

References 13 publications

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“…The Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm [49,50] is used to improve the foreground/background contrast of potentially interesting image subjects. CLAHE calculates histograms of small adjacent regions of pixels that are equalized separately.…”

Section: Methodsmentioning

confidence: 99%

Looking inside the Ocean: Toward an Autonomous Imaging System for Monitoring Gelatinous Zooplankton

Corgnati

Marini

Mazzei

et al. 2016

Sensors

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Marine plankton abundance and dynamics in the open and interior ocean is still an unknown field. The knowledge of gelatinous zooplankton distribution is especially challenging, because this type of plankton has a very fragile structure and cannot be directly sampled using traditional net based techniques. To overcome this shortcoming, Computer Vision techniques can be successfully used for the automatic monitoring of this group.This paper presents the GUARD1 imaging system, a low-cost stand-alone instrument for underwater image acquisition and recognition of gelatinous zooplankton, and discusses the performance of three different methodologies, Tikhonov Regularization, Support Vector Machines and Genetic Programming, that have been compared in order to select the one to be run onboard the system for the automatic recognition of gelatinous zooplankton. The performance comparison results highlight the high accuracy of the three methods in gelatinous zooplankton identification, showing their good capability in robustly selecting relevant features. In particular, Genetic Programming technique achieves the same performances of the other two methods by using a smaller set of features, thus being the most efficient in avoiding computationally consuming preprocessing stages, that is a crucial requirement for running on an autonomous imaging system designed for long lasting deployments, like the GUARD1. The Genetic Programming algorithm has been installed onboard the system, that has been operationally tested in a two-months survey in the Ligurian Sea, providing satisfactory results in terms of monitoring and recognition performances.

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Section: Methodsmentioning

confidence: 99%

Looking inside the Ocean: Toward an Autonomous Imaging System for Monitoring Gelatinous Zooplankton

Corgnati

Marini

Mazzei

et al. 2016

Sensors

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“…Frames with CLAHE_LUV color spaceThe performance metric used to measure the quality enhancement here is the average entropy [27]- [30] value for the sample frames. Entropy measure is the No reference Image Quality assessment metric and higher the value, the richness of details is much more.Here Fig.5 illustrates the visual representation of performance metric, entropy measure for selected frames and the entropy value for selected frames with CLAHE_LUV has shown better improvement.…”

Section: Fig4 Selective Frames 123 Input and Enhanced Outputmentioning

confidence: 99%

Video Frame Illumination Inconsistency Reduction using CLAHE with Kekre’s LUV Color Space

Abin¹,

Thepade²

2020

IJEAT

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 Abstract: Visual frame quality is of utmost significance and is relevant in numerous computer vision applications such as object detection, video surveillance, optical motion capture, multimedia and human computer interface. Under controlled or uncontrolled environment, the video visual frame quality gets affected due to illumination variations. This may further hamper the interpretability and may lead to significant loss of information for background modeling. An excellent background model can enhance good visual perception. In this work, local enhancement technique with improved background modeling, Clipped Adaptive Histogram Equalization (CLAHE) is explored with Kekre's LUV color space to reduce the illumination inconsistency especially with darker set of video frames and a significant improvedaverage entropy of 7.7225 has been obtained, which is higher than the existing explored variations of CLAHE. .

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“…Digital image processing is a branch of signal processing whose objective is to obtain either a processed image or a set of characteristics from a digital image [1]. A digital image is defined as a bi-dimensional function ( , ) that specifies one or more levels of intensity depending on the color model for every coordinate ( , ) or pixel [2].…”

Section: Introductionmentioning

confidence: 99%

“…These techniques improve images through the enhancement of their characteristics. The contrast and noise characteristics are the most frequently mentioned in the literature [1], [3].…”

Section: Introductionmentioning

confidence: 99%

Analyzing pre-processing filters sequences for underwater-image enhancement

Ceballos¹,

Bolaño²,

Sánchez-Torres³

2017

ces

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Enhancement of the quality of a digital image is desirable in several scenarios such as underwater-image analysis, where improving visibility is necessary to reduce alterations caused by unbalanced lighting and the presence of sediments, among others. Many algorithms have been proposed oriented towards treatment of specific factors, such as contrast, color fidelity, noise, and lighting. This work explores the literature techniques and establishes a filter sequence for enhancing underwater images based on a pre-established quality metric. The resulting sequence begins by balancing the lighting using homomorphic filtering, improve the contrast by the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, and finally noise reduction and edge enhancement are done by using bilateral filtering. The results of the implementation suggest a qualitative improvement, in contrast, color, and sharpness of borders.

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Performance Evaluation of Spatial Domain Contrast Enhancement Techniques for Underwater Images

Cited by 10 publications

References 13 publications

Looking inside the Ocean: Toward an Autonomous Imaging System for Monitoring Gelatinous Zooplankton

Looking inside the Ocean: Toward an Autonomous Imaging System for Monitoring Gelatinous Zooplankton

Video Frame Illumination Inconsistency Reduction using CLAHE with Kekre’s LUV Color Space

Analyzing pre-processing filters sequences for underwater-image enhancement

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