Improved Haze Removal of Underwater Images using Particle Swarm Optimization

Sharma, Shriya; Bhalla, Sakshi

doi:10.5120/21687-4792

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…The shrimp video was processed using the deHaze algorithm, which is designed to clarify the object in the video (Figure 1). The dehaze process can increase image visibility and even change the color shift caused by air light [7]. The dehaze process is based on dark channel prior, which derived from the intensity value of color channel is close to zero [8].…”

Section: Datasets Collectionmentioning

confidence: 99%

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Measurement of live shrimp carapace length and its swimming speed in the cage culture using Multiple Object Tracking

Aprianto,

Jaya,

Iqbal

2023

IOP Conf. Ser.: Earth Environ. Sci.

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The carapace length of live shrimp is one of the important parameters to monitor the growth of the shrimp from time to time in cage culture, while its swimming speed is one of the key indicators to understanding the behavior and health of the shrimp. To estimate live shrimp carapace length and its swimming speed, we recorded a live shrimp in the cage culture using an underwater video camera. The collected datasets then being preprocessed and labeled. Before measuring the carapace length of the shrimp, we used the You Only Look Once (YOLO) v5 algorithm with K-fold cross-validation to detect shrimp heads. The accuracy value obtained is 85.6%, recall 85.4%, precision 77.3%, and F1-score 81.1%. Then the multiple object tracking (MOT) DeepSORT method was used to measure the shrimp’s carapace length and its swimming speed. The measurement results showed that the average length of the shrimp was 13.5-14.8 pixels, and the average speed of the shrimp was 62-78 pixels/second. We obtained that there is a strong linear relationship between carapace length and swimming speed of shrimp, namely SS = 11.864 CL + 43.419, where CL and SS are carapace length and swimming speed, respectively, with R-square = 0.9095. In conclusion, the live shrimp carapace length and its swimming speed can be measured using YOLO v5 and MOT DeepSORT directly and accurately.

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Section: Datasets Collectionmentioning

confidence: 99%

“…( 5 ) ( 6 ) ( 7 ) Speed is estimated by calculating the distance between each midpoint on the frame passed by the shrimp and comparing it to the number of frames, which can be written as follows.…”

Section: Carapace Length and Swimming Speed Estimationmentioning

confidence: 99%

Measurement of live shrimp carapace length and its swimming speed in the cage culture using Multiple Object Tracking

Aprianto,

Jaya,

Iqbal

2023

IOP Conf. Ser.: Earth Environ. Sci.

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show abstract

“…As opposed to various approaches that need the explicit road extraction, this technique offers less constraints through virtue of being applicable with no extra than the homogeneous surface extraction containing a portion of the sky and road within the image. [24] Shriya Sharma (2015) et. al., present algorithm of fog elimination is to airlight map estimate for the provide image and then achieve the required operations on the image in order to overcome the fog in the image and improve image quality.…”

Section: Literature Surveymentioning

confidence: 99%

“…It is found that the proposed method is additional appropriate for obtaining higher image quality than the most of the present method. [25] Rajbeer et. al., present that In the calculating parameter the efficient light intensity also provide the scattering estimates of the atmospheric light, combined Laplace of the air-light is and minimum values provide us the basic light map spread which is further used in the intensity restoration.…”

Section: Literature Surveymentioning

confidence: 99%

A Hybrid Technique for Remove Fog from Image using Edges and Color Enhancement Method, Adjustable Empirical Function with Wiener Filter

Chauhan¹

2016

IJSIP

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Regulation of Speed for Driver Assistanceand Removal of Haze using Imageprocessing Algorithm

Delsy

Krishnamoorthy

Rani

et al. 2021

J. Phys.: Conf. Ser.

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The clarity of the outdoor scene images very bad due to the environmental condition result in hazy image. The process of removing the fog from that image is called dehazing. The dehazing process is very difficult in the moving image or video image. The proposed method should improve this problem for the smooth driving of the vehicles by controlling the speed by comparing the image and the scene of video taken during driving. The proposed method should remove the haze so that it will increase the visibility of the image and multi scene. The proposed method can improve the colour changes in the image which is caused by airlight. The single frame is used for the enhancement of the foggy image remove the fog using the transmission map which contains multilevel. We make use of boundary constraints and global airlight estimation is taken into account. The method is fast and noise-free that generally arise in such enhancement techniques.

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Improved Haze Removal of Underwater Images using Particle Swarm Optimization

Cited by 5 publications

References 17 publications

Measurement of live shrimp carapace length and its swimming speed in the cage culture using Multiple Object Tracking

Measurement of live shrimp carapace length and its swimming speed in the cage culture using Multiple Object Tracking

A Hybrid Technique for Remove Fog from Image using Edges and Color Enhancement Method, Adjustable Empirical Function with Wiener Filter

Regulation of Speed for Driver Assistanceand Removal of Haze using Imageprocessing Algorithm

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