Performance analysis of pre-processing filters for underwater images

Srividhya, K.; M.M, Ramya

doi:10.1109/race.2015.7097234

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…Meanwhile, article [17] suggested segmenting selected underwater objects in the images using Canny edge detection and the active contour model. Furthermore, [4] used contrast limited adaptive histogram equalization (CLAHE) in conjunction with an adaptive histogram and a Haar wavelet to reduce noise in underwater images while maintaining their edges. PSNR, contrast to noise ratio (CNR), image enhancement metric (IEM), and absolute mean brightness error (AMBE) are all used to quantify edge detection efficiency.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, image analysis techniques based on computer vision and image processing technologies have been established to monitor marine resources properly. However, no quantitative or qualitative assessment of all underwater marine resources has been conducted, according to [4]. With these constraints in mind, the edge detection process is used as the first computation approach aiming to reduce noise in underwater images, while preserving the important edges.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Various Edge Detection Techniques for Underwater Images

Awalludin¹,

Arsad²,

Yussof³

et al. 2022

JTIT

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Nowadays, underwater image identification is a challenging task for many researchers focusing on various applications, such as tracking fish species, monitoring coral reef species, and counting marine species. Because underwater images frequently suffer from distortion and light attenuation, pre-processing steps are required in order to enhance their quality. In this paper, we used multiple edge detection techniques to determine the edges of the underwater images. The pictures were pre-processed with the use of specific techniques, such as enhancement processing, Wiener filtering, median filtering and thresholding. Coral reef pictures were used as a dataset of underwater images to test the efficiency of each edge detection method used in the experiment. All coral reef image datasets were captured using an underwater GoPro camera. The performance of each edge detection technique was evaluated using mean square error (MSE) and peak signal to noise ratio (PSNR). The lowest MSE value and the highest PSNR value represent the best quality of underwater images. The results of the experiment showed that the Canny edge detection technique outperformed other approaches used in the course of the project.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Various Edge Detection Techniques for Underwater Images

Awalludin¹,

Arsad²,

Yussof³

et al. 2022

JTIT

View full text Add to dashboard Cite

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“…These systems have to be equipped with sensors such as vision cameras, optical-fibers, lasers, etc. to monitor [4] . Robots existing at present for Inspection of structures are:…”

Section: Application Of Robotics For Structural Health Monitoringmentioning

confidence: 99%

Application of Autonomous Robots for Health Monitoring of Structures, A Review

Nair¹,

Tjprc²

2018

IJMPERD

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This paper highlights the status with regard to structural health monitoring and robotic application on structures. This review begins with application of autonomous robot for damage detection and inspection, which goes on to discuss robots that are developed for mobile platform and various sensors employed to sense the defects. This paper includes the application of artificial intelligence that helps in automated data collection and data analysis techniques to improve several aspects of the construction engineering, safety management, risk analysis and prediction. It includes vision based robotic senses for structural health monitoring, vision based structural crack detection, visual based structural displacement monitoring and wireless structural health monitoring. The focus of this review is identifying the structural health monitoring research efforts that include damage detection, risk analysis and structural capacity evaluation. The paper concludes that the criterial gaps includes a lack of validated structural health monitoring that use artificial intelligence to examine structural integrity and predict the remaining life of the structure.

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“…Due to the fast-rising of deep-sea work, underwater image process has attracted more and more attention, especially in the field of ocean engineering, aquatic species counting, underwater archaeology, mining and robotics (1,2) . As sunlight drops off within a certain distance and blue-green color dominates underwater images, artificial lighting is employed for capturing images (3) . The uneven illumination in deep sea brings image analysis challenges.…”

Section: Introductionmentioning

confidence: 99%

Underwater Image Brightness Enhancement based on the DCT Coefficient Model using Genetic Algorithm

Hong

Fan

et al. 2018

Proceedings of the 6th IIAE International Conference on Industrial Application Engineering 2018

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Nowadays underwater image process gains more and more attention, as an increasing number of researchers and companies showing interest in ocean engineering, underwater archaeology, mining etc. The uneven illumination in water brings image analysis a challenge. This paper introduces a new method to optimize brightness based on the DCT coefficient. The statistical model of image DCT coefficients changes with operations on image. Our method is used to enhance the under-exposed and over-exposed regions of images by regulating the low-frequency part of the image DCT coefficients using genetic algorithm.

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Performance analysis of pre-processing filters for underwater images

Cited by 7 publications

References 12 publications

A Comparative Study of Various Edge Detection Techniques for Underwater Images

A Comparative Study of Various Edge Detection Techniques for Underwater Images

Application of Autonomous Robots for Health Monitoring of Structures, A Review

Underwater Image Brightness Enhancement based on the DCT Coefficient Model using Genetic Algorithm

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