HYBRID WAVELET AND CONVOLUTIONAL NEURAL NETWORK APPROACH FOR SIGNAL AND IMAGE PROCESSING

Signal and image processing have become fundamental components of modern intelligent systems, playing a critical role in applications such as computer vision, medical diagnostics, remote sensing, industrial automation, and multimedia communication. Despite significant advancements in deep learning techniques, challenges associated with noise suppression, feature extraction, and computational efficiency remain unresolved. Traditional signal processing methods based on wavelet transforms provide effective multi-resolution analysis and noise reduction capabilities, whereas Convolutional Neural Networks (CNNs) demonstrate superior performance in automatic feature learning and pattern recognition. However, the independent application of these approaches often limits overall processing performance.

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