Executive Summary : | In an attempt to bridge the gap for Human-Computer-Interaction (HCI) and to enable the opportunity for automatic object detection, recognition or localization; sparse image processing plays an indispensable role in fields of machine learning and computer vision applications. From the medical image prospective, the problem of feature selection and retention imposes several challenges including; complex noise distribution, lighting sensitivity, complex tissue structures, robustness to variability and adaptation to different modalities etc. While deep learning has shown promise in denoising, these models can be complex and require substantial computational resources for training and inference. Ensuring the practicality and efficiency of these techniques is important. Moreover, different medical imaging modalities have unique noise characteristics. Developing denoising techniques that can adapt to various modalities and produce consistent results is a challenge. Thus the Traditional methods often struggle to capture fine details and complexities present in medical images. This project proposes a novel approach, Deep Curvelet-Net, that leverages both the power of Convolutional Neural Networks (CNNs) and the discriminative ability of Curvelet feature extraction to enhance medical image analysis and restoration.
The proposed Deep Curvelet-Net architecture holds great promise in advancing medical image analysis and restoration. By combining the strengths of deep learning and Curvelet feature extraction, the approach aims to overcome the limitations of conventional methods and provide more accurate and informative results. |