Skip to main content

Underwater Image Processing 18of20



For more details: https://open.substack.com/pub/veryshorttermcourse/p/internship-3-underwater-image-processing?r=c8bxy&utm_campaign=post&utm_medium=web
"Submarine underwater image processing" leverages advanced techniques like AI and deep learning to enhance the quality of images captured by underwater drones and submarines, enabling detailed analysis of the ocean floor through "AI-assisted underwater mapping." This technology utilizes "deep learning for ocean floor analysis," allowing for automated identification of marine life, geological features, and potential hazards. By applying "underwater drone image enhancement" algorithms, researchers can overcome the inherent challenges of underwater imaging, including low visibility and color distortion, leading to improved "underwater image quality improvement with AI." 

This has significant implications for both scientific research, with applications in marine biology and environmental monitoring, and defense operations, where "real-time underwater image processing for defense" is crucial for surveillance and navigation. However, "challenges of underwater image processing" like light scattering and turbidity must be addressed to achieve optimal results in diverse underwater environments.

Key points:
  • Image enhancement:
    AI algorithms are used to improve the clarity and color accuracy of underwater images captured by submarines and drones.
  • Ocean floor mapping:
    Deep learning models can analyze underwater imagery to create detailed maps of the seabed, identifying geological features and potential hazards.
  • Scientific applications:
    Researchers can study marine life, monitor coral reef health, and assess environmental changes with enhanced underwater imagery.
  • Defense applications:
    Real-time image processing allows for underwater surveillance and navigation in challenging environments.
  • Technical challenges:
    Addressing issues like light attenuation, backscatter, and turbidity is crucial for accurate underwater image analysis.

Popular posts from this blog

Five open source free hydrologic models that you can use to model runoff of micro to macro watersheds

The principal objective of hydrologic models is to forecast the runoff of a surface water body, especially dendritic systems like rivers, streams, etc. The inputs to these models are generally Rainfall/Precipitation, Soil Characteristics, and other Climatic parameters like evapotranspiration, humidity, etc. LULC and geo-morphology are also used as the required input parameters of the hydrologic models. Both input and output of these models are temporally as well as spatially variable. Now the resolution varies with different models. Some models consider all the sub-basins to be a single watershed and determine the output based on the characteristics of this single watershed(lumped).In contrast, some other models will consider the {impact|effect} of each of the sub-basins on the central outflow of the watershed(distributed).In a few models, the entire watershed is divided into grids or units of uniform dimension. However, the accuracy is highest for the models, which considers the {impa...

Autocorrelation in Water Resource Development

A new article was posted in HydroGeek : Five Most Recent Research Works on Autocorrelation in Water Resource Management Autocorrelation is the correlation between two part of a single data series and is useful when the trendability of a parameter is approximated with the help of data. Most used in water research study. This article highlights the most recent research works on the application of autocorrelation on water resource development studies. Click here to read it in HydroGeek @Mrinmoy's Page @data_hydrology , @Merchandise or @ @products_sustainability Add to Listy /

First Edition of HydroGeek Newsletter for the year 2023 Launched

First Edition of HydroGeek Launched You will be happy to know that the first edition of the HydroGeek Newsletter of the year 2023 is launched The content of the first edition is as given below : Cover Feature: The Free Software for Water Resource Management Feature 1: A case study of the ELECTRE Decision-Making Method in Water Resource: How to use the technique in the selection of the best solution among the available many. Feature 2: Project Idea on Climate Change Impact Studies Feature 3: Instrument Recommendation: An instrument that can monitor more than seven water quality parameters in real-time Regular Features: News and Views, Recommended New and Old Books, More Project Ideas, etc. Click here to access it. #hydrology #hydroinformatics #newsletter @data_hydrology , @Merchandise or @ @products_sustainability Add to Listy /