Highly Accurate Image Reconstruction for Multimodal Noise Suppression Using Semisupervised Learning on Big Data
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...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2017
|
| Online Access: | http://ndltd.ncl.edu.tw/handle/e9gnre |
| Summary: | 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.
|
|---|