Paper PublishingSingle Image Deraining Using Bilateral Recurrent Network
Objective
In this approach we first propose a single recurrent network (SRN) by recursively unfolding a shallow residual network, for extracting rain streaks and also for learning direct mapping for predicting clean background image. Then, we propose bilateral LSTMs, which not only can respectively propagate deep features of rain streak layer and background image layer across stages, but also bring the interplay between these two SRNs, finally forming bilateral recurrent network (BRN).
Abstract
Removing rain streaks plays an important role in many computer vision applications in rainy outdoor scenes, e.g., surveillance, object detection and recognition. Single image deraining is a very challenging. Basically, image deraining can be treated as an image decomposition problem.
To overcome this problem, we propose bilateral LSTMs, which not only can respectively propagate deep features of rain streak layer and background image layer across stages, but also bring the interplay between these two SRNs, finally forming bilateral recurrent network (BRN).
The proposed methods also perform more favourably in terms of generalization performance on real-world rainy dataset. We also proposed a simple yet effective single recurrent network (i.e., SRN) for image deraining.
Keywords: Image De-raining, Convolutional Neural Network, Recurrent Network, LSTM.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
HARDWARE SPECIFICATIONS:
- Processor: I3/Intel
- Processor RAM: 4GB (min)
- Hard Disk: 128 GB
- Key Board: Standard Windows Keyboard
- Mouse: Two or Three Button Mouse
- Monitor: Any
SOFTWARE SPECIFICATIONS:
- Operating System: Windows 7+
- Server-side Script: Python 3.6+
- IDE: PyCharm
- Libraries Used:Pandas, Numpy, os, Tensorflow, Flask, OpenCV.
Learning Outcomes
- What is a single recurrent network?
- Conventional neural network.
- What is bilateral LSTMs?
- Image deraining.
- Importance of PyCharm IDE.
- Implementing a CNN model.
- Process of debugging a code.
- The problem with imbalanced datasets.
- Benefits of SMOTE technique.
- Input and Output modules.
- How test the project based on user inputs and observe the output?
- Work on Creative ideas.
- Using OpenCV for managing images.
- 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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