Project Background:
Compressive sensing using deep learning is rooted in the quest for efficient signal reconstruction from sparse measurements, a fundamental challenge in various fields such as imaging, signal processing, and telecommunications. Compressive sensing offers a revolutionary approach by exploiting the inherent sparsity or compressibility of signals to recover them accurately from significantly fewer measurements than traditional methods would require. In this context, the project aims to synergize compressive sensing principles with deep learning methodologies to enhance the reconstruction quality and efficiency of sparse signals. By training deep neural networks on datasets containing sparse measurements and their corresponding signals, the system can learn to effectively reconstruct signals from limited measurements, even in noise and distortions. Moreover, it offers the advantage of adaptability and generalization, enabling them to handle diverse signal types and adapt to varying sensing scenarios.
- Traditional methods for signal reconstruction from sparse measurements often struggle with accuracy and efficiency.
- Recovering signals from a limited number of measurements while maintaining fidelity is challenging, especially in noisy environments.
- The inherent complexity of signal structures and noise characteristics complicates the reconstruction process using conventional techniques.
- Existing approaches may lack scalability, particularly when dealing with high-dimensional or large-scale signal data.
- Demand for methods can adapt to various signal types, noise levels, and sensing scenarios without sacrificing performance.
Aim and Objectives
- To enhance signal reconstruction efficiency and accuracy using deep learning-based approaches in compressive sensing.
- Develop deep learning models capable of accurately reconstructing sparse signals from limited measurements.
- Improve reconstruction quality by integrating deep learning techniques to handle noise and distortions in the measured data.
- Investigate the scalability of deep learning-based compressive sensing methods for high-dimensional and large-scale signal data.
- Explore the adaptability of deep learning models to various signal types, noise levels, and sensing scenarios.
- Validate the effectiveness of the developed approaches through rigorous testing and comparison with existing methods using benchmark datasets and real-world applications.
Contributions to Compressive Sensing using Deep Learning
- Improved the efficiency and accuracy of signal reconstruction from sparse measurements by leveraging deep learning techniques.
- Developed methods to enhance reconstruction quality by effectively handling noise and distortions in measured data.
- Explored deep learning-based approaches that are scalable to high-dimensional and large-scale signal datasets, facilitating practical applications in diverse domains.
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Investigated deep learning models adaptability to various signal types, noise levels, and sensing scenarios, enhancing their versatility and applicability.
- Validated the effectiveness of the proposed methods through rigorous testing and comparison with existing techniques using benchmark datasets and real-world applications.
Deep Learning Algorithms for Compressive Sensing
- Convolutional Neural Networks (CNNs)
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Recurrent Neural Networks (RNNs)
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Autoencoders
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Generative Adversarial Networks (GANs)
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Variational Autoencoders (VAEs)
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Sparse Coding Neural Networks
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Deep Belief Networks (DBNs)
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Denoising Autoencoders
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Long Short-Term Memory networks (LSTMs)
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Graph Neural Networks
Datasets for Compressive Sensing using Deep Learning
- MNIST
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CIFAR-10
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CelebA
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ImageNet
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COCO
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SVHN (Street View House Numbers)
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Pascal VOC
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Fashion-MNIST
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KITTI
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Berkeley Segmentation Dataset (BSDS500)
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1.Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2.Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
