Adams Optimized Image Restoration Using Multi-Level Wavelet CNN with Added Noise
S Manikanta, V Vyshnavi, T Tharani Pragna, T Anupama Salmon, R Saibaba, B Elisha Raju · 2024
In fundamental image recognition processes in particular, striking a fine balance between the amount of information an AI can process from images and its processing speed is crucial. Regular convolutional networks often process information more slowly but perceive more details. In an attempt to address this, DWT has been missing pieces and producing strange patterns lately. In this section, we will present a brand-new Multi-Level Wavelet CNN (MWCNN) model. It strikes a better mix between productivity and seeing a lot. We'll employ an alternative architecture, such as the U-Net, and incorporate the wavelet transform to reduce the amount of information the AI processes. It reduces processing without losing crucial information. Similar to a better dilated filtering technique, our MWCNN can resolve a variety of picture problems. We believe that our approach is quite effective in reducing noise in photographs, improving the clarity of tiny images, and resolving issues with JPEG images.