Compressive Sensing for Image Reconstruction: A Deep Neural Network Approach
Keywords:
Deep learning, Compressive sensing, image reconstruction, Neural NetworksAbstract
This research explores the application of Compressive Sensing (CS) for image reconstruction, introducing a novel approach based on Deep Neural Networks (DNN). Compressive Sensing is a technique employed to recover sparse signals or images from a small number of measurements, providing an efficient alternative to traditional image acquisition methods. In this paper, the capability of Deep Neural Networks to enhance the reconstruction process within the Compressive Sensing framework is proposed. The approach involves training a deep neural network, the intricate mapping between the matching high-resolution images and compressed measurements may be learned. Taking advantage of the innate patterns and structures found in pictures, the DNN aims to reconstruct the original content from highly under sampled measurements, demonstrating the potential of neural networks in addressing the challenges posed by sparse signal recovery. The paper provides an in-depth analysis of the proposed Compressive Sensing with Deep Neural Network (CS-DNN) approach, evaluating its performance against existing methods through comprehensive experiments. The result shows the effectiveness and versatility of the proposed technique, highlighting its potential to outperform traditional CS methods in terms of both image quality and computational efficiency. This research contributes to advancing the field of image reconstruction by integrating the power of Deep Neural Networks into the Compressive Sensing paradigm, opening new avenues for efficient and robust sparse signal recovery in various applications.
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