Image Denoising via Sequential Ensemble Learning

Project Code :TCPGPY368

Objective

This paper mainly aims at investigating the application of ensemble learning in image denoising, we combine a set of simple base denoisers to form a more effective image denoiser. Based on different types of image priors, two types of base denoisers in the form of transform-shrinkage are proposed for constructing the ensemble.

Abstract

This paper presents denoising of image using the convolutional neural network (CNN) model in deep learning. It has become an important task to remove noise 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 1% to 10% noise to the image and then applying CNN model to denoise it. Further, qualitative and quantitative analysis of the denoised image is performed. Here the CNN model mainly consists of the encoder and decoder layers that which will help in making the image to be denoised. The results from the analysis and experiment show that the CNN model can efficiently remove noise and restore the image details and data than any other traditional/standard image filtering techniques.


Keywords: Image denoising, 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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