Unified framework for noise reduction in both audio and image domains using advanced deep learning techniques, specifically a U-Net architecture. The application is designed to facilitate the denoising process for various types of noise, including Salt and Pepper, Gaussian, Impulse, White Noise, and Environmental Noise in audio signals, as well as for noisy images. The framework leverages TensorFlow and Keras for model training and inference, utilizing U-Net models for audio and image denoising tasks. Users can record or upload noisy audio files, which are processed through a series of denoising techniques, including spectral subtraction, Wiener filtering and U-Net. The denoised audio can be played back, visualized, and saved for further use. In the image processing module, users can load noisy images and their corresponding ground truth images. The application employs a U-Net model to predict and display denoised images, allowing for visual comparison with the original noisy images. Additionally, edge detection is performed on the images using the Canny edge detection algorithm, providing insights into the structural integrity of the denoised outputs. The graphical user interface (GUI) is built using Tkinter, offering an intuitive experience for users to navigate through audio and image processing functionalities.
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