Super-Resolution Based on Advanced Weighting and Learning Techniques

• Main Authors: Yi-Wen Chen, 陳意雯
• Other Authors: 丁建均
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/dt3yzq

碩士 === 國立臺灣大學 === 電信工程學研究所 === 107 === Nowadays, digital images are easy to access, and high-resolution images are often required for later image processing and analysis. However, the spatial resolution of images captured by digital cameras is limited by principles of optics and the size of imaging sensors. While constructing optical components that can capture very high-resolution images is prohibitively expensive and impractical, image super-resolution (SR) provides a convenient and economical solution. Image super-resolution aims to generate a high-resolution (HR) image from a low-resolution (LR) input image. It is an essential task in image processing and can be utilized in many high-level computer vision applications, such as video surveillance, medical diagnosis and remote sensing. Super-resolution is an ill-posed problem since multiple HR images could correspond to the same LR image. In this thesis, we propose two algorithms for image super-resolution. The first one is to combine and take advantage of different image super-resolution methods while the second one is based on deep learning. Conventional image super-resolution methods, including bilinear interpolation and cubic convolution interpolation, are intuitive and simple to use. However, they often suffer from artifacts such as blurring and ringing. To deal with this problem, we propose a weighting-based algorithm that takes advantage of three different image super-resolution methods and generates the final results from the combination of these methods. We extract features of the input LR image and investigate the performance of the chosen methods under different features. Results from the candidate methods are combined using a weighted average based on the statistical values of the training data. As the development of convolutional neural networks and deep learning in recent years, models trained on large scale of datasets achieve favorable performance on many computer vision applications. In this thesis, we propose another deep learning-based approach for image super-resolution. We use the wavelet transform to separate the input image into four frequency bands, and train a model for each sub-band. By processing information from different frequency bands via different CNN models, we can extract features more efficiently and learn better LR-to-HR mappings. In addition, we add dense connection to the model to make better use of the internal features in the CNN model. Furthermore, geometric self-ensemble is applied in the testing stage to maximize the potential performance.