Highly Accurate Image Reconstruction for Multimodal Noise Suppression Using Semisupervised Learning on Big Data

• Main Authors: Jia-Li Yin, 印佳麗
• Other Authors: Bo-Hao Chen
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/e9gnre

碩士 === 元智大學 === 資訊工程學系 === 106 === Impulse noise corruption in digital images frequently occurs because of errors generated by noisy sensors or communication channels, such as faulty memory locations in devices, malfunctioning pixels within a camera, or bit errors in transmission. Although recently developed big-data streaming enhances the viability of video communication, visual distortions in images caused by impulse noise corruption can negatively affect video communication applications. This thesis presents a novel model that uses a devised cost function involving semisupervised learning based on a vast amount of corrupted image data with a few labeled training samples to effectively remove the visual effects of impulse noise from the corrupted images. In the experiments, the proposed model qualitatively and quantitatively outperformed the existing state-of-the-art image reconstruction models in terms of both effectiveness and the denoising effect.