Underwater image quality enhancement of sea cucumbers based on improved histogram equalization and wavelet transform

Qiao, Xi; Bao, Jianmin; Zhang, Hang; Zeng, Lihua; Li, Daoliang

doi:10.1016/j.inpa.2017.06.001

Cited by 38 publications

(19 citation statements)

References 29 publications

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“…A similar work utilizes the Rayleigh distribution to modify by modifying the histogram in RGB and HSV colour spaces and combine the results (Ghani and Isa, 2014). Qiao et al (2017) built on the previous work to improve contrast and added wavelet transformation for better denoising of images of underwater cucumbers. The efficiency of this method was qualitatively and quantitatively evaluated on 120 greyscale underwater images.…”

Section: Enhancement Of Underwater Imagesmentioning

confidence: 99%

3d Reconstruction and Mesh Optimization of Underwater Spaces for Virtual Reality

Saer¹,

Stentoumis²,

Kalisperakis³

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this contribution, we propose a versatile image-based methodology for 3D reconstructing underwater scenes of high fidelity and integrating them into a virtual reality environment. Typically, underwater images suffer from colour degradation (blueish images) due to the propagation of light through water, which is a more absorbing medium than air, as well as the scattering of light on suspended particles. Other factors, such as artificial lights, also, diminish the quality of images and, thus, the quality of the image-based 3D reconstruction. Moreover, degraded images have a direct impact on the user perception of the virtual environment, due to geometric and visual degenerations. Here, it is argued that these can be mitigated by image pre-processing algorithms and specialized filters. The impact of different filtering techniques on images is evaluated, in order to eliminate colour degradation and mismatches in the image sequences. The methodology in this work consists of five sequential pre-processes; saturation enhancement, haze reduction, and Rayleigh distribution adaptation, to de-haze the images, global histogram matching to minimize differences among images of the dataset, and image sharpening to strengthen the edges of the scene. The 3D reconstruction of the models is based on open-source structure-from-motion software. The models are optimized for virtual reality through mesh simplification, physically based rendering texture maps baking, and level-of-details. The results of the proposed methodology are qualitatively evaluated on image datasets captured in the seabed of Santorini island in Greece, using a ROV platform.

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Section: Enhancement Of Underwater Imagesmentioning

confidence: 99%

3d Reconstruction and Mesh Optimization of Underwater Spaces for Virtual Reality

Saer¹,

Stentoumis²,

Kalisperakis³

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Where k n is for the number of pixels in the image with gray scale k r , N is for the sum of the numbers of all the pixels, and L is for the number of gray scale k r . Gray scale k r and the probability of appearance of gray scale ( ) k p r can be expressed as the histogram of the original image [46].…”

Section: Image Preprocessingmentioning

confidence: 99%

Pavement Damage Crack Recognition Method Based on High-Resolution Linear Array Cameras and Adaptive Lifting Algorithm

Han¹,

Tu²,

Yang³

et al. 2018

Preprint

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“…Furtherly, in order to improve the accuracy of identification and location, Xi Qiao et al used the principal component analysis and support vector machine to locate the underwater sea cucumbers [13]. In order to reduce the influence of the disturbance of seabed environment on the identification accuracy of sea cucumbers, Xi Qiao et al used the limited adaptive histogram equalization and wavelet transform (CLAHE-WT) to identify and locate the sea cucumbers, which improved the identification accuracy of sea cucumbers in the presence of environmental disturbances [14].…”

Section: Introductionmentioning

confidence: 99%

Detection and Analysis of Behavior Trajectory for Sea Cucumbers Based on Deep Learning

Jiang

et al. 2020

IEEE Access

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The motion trajectory of sea cucumbers reflects the behavior of sea cucumbers, and the behavior of sea cucumbers reflects the status of the feeding and individual health, which provides the important information for the culture, status detection and early disease warning. Different from the traditional manual observation and sensor-based automatic detection methods, this paper proposes a detection, location and analysis approach of behavior trajectory based on Faster R-CNN for sea cucumbers under the deep learning framework. The designed detection system consists of a RGB camera to collect the sea cucumbers' images and a corresponding sea cucumber identification software. The experimental results show that the proposed approach can accurately detect and locate sea cucumbers. According to the experimental results, the following conclusions are drawn: (1) Sea cucumbers have an adaptation time for the new environment. When sea cucumbers enter a new environment, the adaptation time is about 30 minutes. Sea cucumbers hardly move within 30 minutes and begin to move after about 30 minutes. (2) Sea cucumbers have the negative phototaxis and prefers to move in the shadows. (3) Sea cucumbers have a tendency to the edge. They like to move along the edge of the aquarium. When the sea cucumber is in the middle of the aquarium, the sea cucumber will look for the edge of the aquarium. (4) Sea cucumbers have unidirectional topotaxis. They move along the same direction with the initial motion direction. The proposed approach will be extended to the detection and behavioral analysis of the other marine organisms in the marine ranching. INDEX TERMS Artificial intelligence (AI), animal behavior, deep learning, object detection, faster R-CNN, marine ranching, sea cucumber.

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Underwater image quality enhancement of sea cucumbers based on improved histogram equalization and wavelet transform

Cited by 38 publications

References 29 publications

3d Reconstruction and Mesh Optimization of Underwater Spaces for Virtual Reality

3d Reconstruction and Mesh Optimization of Underwater Spaces for Virtual Reality

Pavement Damage Crack Recognition Method Based on High-Resolution Linear Array Cameras and Adaptive Lifting Algorithm

Detection and Analysis of Behavior Trajectory for Sea Cucumbers Based on Deep Learning

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