Paper PublishingVideo Image Deblurring Algorithm Based on Denoising Engine
Also Available Domains Deep Learning
Objective
A good video deblurring effect and certain robustness to noise is suggested in this work
Abstract
A video image deblurring approach based on a denoising engine is suggested to address the problem of blurred digital video losing inter-frame information and ignoring spatiotemporal during restoration. The denoising engine's adaptive Laplacian regularization term is extended to the realm of video image restoration.
First, we use the nonlocal means (NLM) regularization to extract redundant information from video images, and then we
provide a novel restoration model that combines several regularizes,
particularly the NLM regularize and the denoising regularize.
We employed the
simplest gradient descent approach to solve the video image restoration model. The
results of the experiments reveal that our approach has an excellent deblurring
effect and is noise resistant.
Keywords:
Regularization
by denoising, NLM, self-similarity video image deblurring
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Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
- Introduction to Matlab
- What is EISPACK & LINPACK
- How to start with MATLAB
- About Matlab language
- Matlab coding skills
- About tools & libraries
- Application Program Interface in Matlab
- About Matlab desktop
- How to use Matlab editor to create M-Files
- Features of Matlab
- Basics on Matlab
- What is an Image/pixel?
- About image formats
- Introduction to Image Processing
- How digital image is formed
- Importing the image via image acquisition tools
- Analyzing and manipulation of image.
- Phases of image processing:
- Acquisition
- Image enhancement
- Image restoration
- Color image processing
- Image compression
- Morphological processing
- Segmentation etc.,
- About Artificial
Intelligence (AI)
- About Machine
Learning
- About Deep
Learning
- About layers in
AI (input, hidden and output layers)
- Building AI
(ANN/CNN) architecture using Matlab
- We will
able to know, what’s the term “Training” means in Artificial Intelligence
- About requirements that can influence the AI training process:
- Data
- Training data
- Validation data
- Testing data
- Hardware requirements to train network
- How to detect an object using AI
- How to extend our work to another real time applications
- Project development Skills
- Problem analyzing skills
- Problem solving skills
- Creativity and imaginary skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
- Thesis writing skills
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