Learning Deep Gradient Descent Optimization for Image Deconvolution

Project Code :TCMAPY257

Objective

In this project, We propose a Recurrent Gradient Descent Network (RGDN) by systematically incorporating deep neural networks into a fully parameterized gradient descent scheme that which can enhance the blurred image to the clean image. The proposed deep optimization method is effective and robust to produce favorable results as well as practical for real-world image deblurring applications.

Abstract

This paper presents deblurring of image using the convolutional neural network (CNN) model in deep learning. It has become an important task to remove noise and blurs from the image and restore a high-quality image in order to process image further for the purpose like object segmentation, detection, tracking etc. This analysis is done by adding blur noise to the image and then applying CNN model to deblur it.  Here the CNN model mainly consists of the encoder and decoder layers that which will help in making the image to be deblur. The results from the analysis and experiment show that the CNN model can remove blur and restore the image details and data than any other traditional/standard image filtering techniques.


Keywords: Image deblurring, blur noise, convolutional neural network, Deep Learning

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

H/W Configuration:

      Processor                    :    I3/Intel Processor

      Hard Disk                    :   160GB

      RAM                             :    8Gb

 

S/W Configuration:

      Operating System       :   Windows 7/8/10            .          

      IDE                                :   Pycharm.

      Libraries Used            :    Numpy, IO, OS, keras.

      Technology                 :    Python 3.6+.

Learning Outcomes

  •          Practical exposure to
      •          Hardware and software tools
      •          Solution providing for real time problems
      •          Working with team/individual
      •          Work on creative ideas

  •          Testing techniques
  •          Error correction mechanisms
  •          What type of technology versions is used?
  •          Working of Tensor Flow
  •          Implementation of Deep Learning techniques
  •          Working of CNN algorithm
  •          Building of model creations
  •          Scope of project
  •          Applications of the project
  •          About Python language
  •          About Deep Learning Frameworks
  •          Use of Data Science

 

 

 

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