Deep Learning Approaches for Signal and Image Processing: State-of-the-Art and Future Directions

Deep learning has revolutionized signal and image processing by enabling the creation of complex algorithms with many applications. This study examines deep learning signal and image processing optimization and hardware acceleration strategies. Experimentally evaluating stochastic gradient descent (SGD) and Adam optimization algorithms determines their convergence speed and effectiveness. We also examine how GPUs may accelerate model execution and deep learning inference. Our study reveals that deep learning optimization methodologies and platforms provide several practical challenges and trade-offs. This work may help signal and image processing researchers and practitioners design scalable and efficient solutions. Additionally, their methods illuminate deep learning.