New Approach to Underwater Image Dehazing using Dark Channel Prior

Ghate, Shubhangi N.; Nikose, Mangesh

doi:10.1088/1742-6596/1937/1/012045

Cited by 4 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Colours associated with short wavelengths, such as red and orange, were retrieved using a Red Channel technique [10]. An improvement on the previous dark channel prior [16] was introduced by Drews et al [18] with the UDCP underwater dark-channel prior. When white or artificial light is present in underwater scenes, the UDCP may be able to estimate medium transmission in some cases.…”

Section: B) Underwater Image Restoration Methodmentioning

confidence: 99%

Optimizing Underwater Vision: A Rigorous Investigation into CNN's Deep Image Enhancement for Subaquatic Scenes

Shubhangi N. Ghate

2024

jes

View full text Add to dashboard Cite

In this paper, Convolutional Neural Networks were used to enhance the visual fidelity of underwater images. The UWCNN is introduced in this article, which utilizes underwater scene priors and a CNN model to improve underwater photos. The UWCNN model proposes a method where the clear latent underwater image is immediately rebuilt using the underwater scene as training data, rather than relying on parameter guessing in an underwater imaging model. Our UWCNN model may be used for frame-by-frame augmentation in underwater videos because to its lightweight network structure and efficient training data. In this dataset for underwater image deterioration by integrating an underwater photography physical model with optical characteristics of underwater landscapes are provided which includes a wide range of water types and degradation levels. Alternatively, one might choose to go light. A Neural Network (CNN) model is constructed using the related training data in order to enhance the quality of underwater scenes. Ultimately, the UWCNN model is used to improve the quality of underwater videos. The efficacy of our technology is shown via the analysis of both authentic and synthetic underwater photos.

show abstract

Section: B) Underwater Image Restoration Methodmentioning

confidence: 99%

Optimizing Underwater Vision: A Rigorous Investigation into CNN's Deep Image Enhancement for Subaquatic Scenes

Shubhangi N. Ghate

2024

jes

View full text Add to dashboard Cite

show abstract

“…Thus, to address these issues, discrete wavelet transform-based GAN (DW-GAN) [20] has been implemented to remove non-homogeneous weld fumes because dense welding fumes affect the path to be traced while welding. The GAN-based technique is compared with the conventional dark channel prior (DCP) method [21].…”

Section: Dehazing Techniques For Weld Fume Removalmentioning

confidence: 99%

GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning

et al. 2022

View full text Add to dashboard Cite

Weld seam identification with industrial robots is a difficult task since it requires manual edge recognition and traditional image processing approaches, which take time. Furthermore, noises such as arc light, weld fumes, and different backgrounds have a significant impact on traditional weld seam identification. To solve these issues, deep learning-based object detection is used to distinguish distinct weld seam shapes in the presence of weld fumes, simulating real-world industrial welding settings. Genetic algorithm-based state-of-the-art object detection models such as Scaled YOLOv4 (You Only Look Once), YOLO DarkNet, and YOLOv5 are used in this work. To support actual welding, the aforementioned architecture is trained with 2286 real weld pieces made of mild steel and aluminum plates. To improve weld detection, the welding fumes are denoised using the generative adversarial network (GAN) and compared with dark channel prior (DCP) approach. Then, to discover the distinct weld seams, a contour detection method was applied, and an artificial neural network (ANN) was used to convert the pixel values into robot coordinates. Finally, distinct weld shape coordinates are provided to the TAL BRABO manipulator for tracing the shapes recognized using an eye-to-hand robotic camera setup. Peak signal-to-noise ratio, the structural similarity index, mean square error, and the naturalness image quality evaluator score are the dehazing metrics utilized for evaluation. For each test scenario, detection parameters such as precision, recall, mean average precision (mAP), loss, and inference speed values are compared. Weld shapes are recognized with 95% accuracy using YOLOv5 in both normal and post-fume removal settings. It was observed that the robot is able to trace the weld seam more precisely.

show abstract

“…The existing underwater restoration strategies have sprung up in recent years. Several foggy dehazing techniques [9] have been introduced with great attention. However, underwater imagery is even more difficult to handle the extinction resulting from scattering depending on the light wavelength on the color component.…”

Section: Introductionmentioning

confidence: 99%

A Fusion Underwater Salient Object Detection Based on Multi-Scale Saliency and Spatial Optimization

Huang,

Zhu,

Chen

2023

JMSE

View full text Add to dashboard Cite

Underwater images contain abundant information, but many challenges remain for underwater object detection tasks. Various salient object detection methods may encounter low detection precision, and the segmented map has an incomplete region of the target object. To deal with blurry underwater scenes and vague detection problems, a novel fusion underwater salient object detection algorithm (FUSOD) is proposed based on multi-scale saliency and spatial optimization. Firstly, an improved underwater color restoration was utilized to restore the color information for afterward color contrast saliency calculation. Secondly, a more accurate multi-scale fusion saliency map was obtained by fully considering both the global and local feature contrast information. Finally, the fusion saliency was optimized by the proposed spatial optimization method to enhance the spatial coherence. The proposed FUSOD algorithm may process turbid and complex underwater scenes and preserve a complete structure of the target object. Experimental results on the USOD dataset show that the proposed FUSOD algorithm can segment the salient object with a comparatively higher detection precision than the other traditional state-of-the-art algorithms. An ablation experiment showed that the proposed spatial optimization method increases the detection precision by 0.0325 scores in the F-Measure.

show abstract

New Approach to Underwater Image Dehazing using Dark Channel Prior

Cited by 4 publications

References 8 publications

Optimizing Underwater Vision: A Rigorous Investigation into CNN's Deep Image Enhancement for Subaquatic Scenes

Optimizing Underwater Vision: A Rigorous Investigation into CNN's Deep Image Enhancement for Subaquatic Scenes

GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning

A Fusion Underwater Salient Object Detection Based on Multi-Scale Saliency and Spatial Optimization

Contact Info

Product

Resources

About