Multi scale progressive fusion network for single image deraining

Project Code :TCMAPY417


The main objective of the project is to remove the rain in the images using convolution neural network


We propose a new deep network architecture for removing rain streaks from individual images based on the deep convolutional neural network (CNN). Inspired by the deep residual network (ResNet) that simplifies the learning process by changing the mapping form, we propose a deep detail network to directly reduce the mapping range from input to output, which makes the learning process easier. To further improve the de-rained result, we use a priori image domain knowledge by focusing on high frequency detail during training, which removes background interference and focuses the model on the structure of rain in images. This demonstrates that a deep architecture not only has benefits for high-level vision tasks but also can be used to solve low level imaging problems. Though we train the network on synthetic data, we find that the learned network generalizes well to real-world test images. Experiments show that the proposed method significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures.

Keywords: Image Deraining, Convolution Neural Network (CNN), 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 Configuration:

Processor: I3/Intel Processor

Hard Disk: 160GB

RAM: 8Gb

S/W Configuration:

Operating System: Windows 7/8/10            .          

Server side Script: HTML, CSS & JS.

IDE: Pycharm.

Libraries Used: Numpy, IO, OS, Flask, 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

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