Image Denoising using Auto Encoders and Decoders

Project Code :TCMAPY523


The main objective of this project is to denoise the image using the auto encoders and decoders.


This paper presents the denoising of images 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 the image further for purposes like object segmentation, detection, tracking etc. This analysis is done by adding 1% to 10% noise to the image and then applying the 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 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 technique.

Keywords: Image denoising, noise, convolutional neural network, Auto encoders and decoders, 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


H/W Specifications:

  • Processor  :  I5/Intel Processor
  • RAM   :  8GB (min)
  • Hard Disk  :  128 GB

S/W Specifications:

  • Operating System : Windows 10
  • Server-side Script  :  Python 3.6
  • IDE  :  PyCharm
  • Libraries Used : Numpy, CV2, OS, Keras, pandas, tensorflow

Learning Outcomes

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

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