Underwater Image Enhancement Using Convolutional Neural Network
Underwater Image Enhancement using Deep Learning Underwater Image Enhancement project will do comparative study about different technique that will help for improving image enhancement, These images of underwater is suffer from motion blur effect due to turbulence in non-uniform illumination in the flow of water and limited contrast. Due to distorted images captured from underwater to be preprocessed in different ways because the underwater images captured in deep low light and worst quality image these are low contrast cause limited range visibility, blurring effect, absorption, scattering and hazy and light transportation is limited so quality of image degrades so cannot be directly used for various deep learning or computer vision techniques. Image Enhancement is to bring more visibility in image.
Input Image:-
- ~An Image from dataset
Output Image:-
- ~White Balance Images
- ~Histogram Equalization Images
- ~Gamma Correction Image
- ~Analysis Result
Deep CNN method for Underwater Image Enhancement
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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