Image Super Resolution, Image Alpha Estimation, and Metric Learning for Image Classification

• Main Authors: Lin, Fang-Ju, 林芳如
• Other Authors: Chuang, Jen-Hui
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/w6g95t

博士 === 國立交通大學 === 資訊科學與工程研究所 === 107 === Computer vision technology uses digital cameras to simulate human vision, computer programs and algorithms to simulate people's understanding and thinking about things. Computer vision algorithms combine a wide range of disciplines such as artificial intelligence, machine learning, image processing, and neurobiology. In this thesis, we discuss three different applications combined with machine learning and pattern recognition algorithms, from pixel, patch, and feature units of the image to achieve better results than traditional image processing algorithms. In the first part, image super resolution is the process of generating a high-resolution (HR) image using one or more low-resolution (LR) inputs. Many SR methods have been proposed but generating the small-scale structure of an SR image remains a challenging task. We hence propose a single-image SR algorithm that combines the benefits of both internal and external SR methods. First, we estimate the enhancement weights of each LR-HR image patch pair. Next, we multiply each patch by the estimated enhancement weight to generate an initial SR patch. We then employ a method to recover the missing information from the high-resolution patches and create that missing information to generate a final SR image. We then employ iterative back-projection to further enhance visual quality. The method is compared qualitatively and quantitatively with several state-of-the-art methods, and the experimental results indicate that the proposed framework provides high contrast and better visual quality, particularly for non-smooth texture areas. The primary focus of the second part was to present a new approach for extracting foreground elements from an image by means of color and opacity (alpha) estimation which considers available samples in a searching window of variable size for each unknown pixel. Alpha-matting is conventionally defined as the endeavor of softly extracting foreground objects from a single input image and plays a central role within the realm of image-processing. In particular, the challenging case of natural image matting has received considerable research attention since there are virtually no restrictions for characterizing background regions. Many algorithms are presently available for estimating foreground samples and background samples for all unknown pixels of an image, along with opacity values. Given a trimap configuration of background/foreground/unknown regions of an input image, a straightforward approach for determining an alpha value is to sample (collect) unknown foreground and background colors for each unknown pixel defined in the trimap. Such a proposed sampling method is robust in that similar sampling results can be generated for input trimaps of different unknown regions. Moreover, after an initial estimation of the alpha matte, a fully-connected conditional random field (CRF) can be adopted to correct a predicted matte at the pixel level. In the third part, we developed effective weather features to solve the problem of weather recognition using a metric learning method. The recognition of weather conditions based on single image in large datasets is a challenging problem in computer vision. Although previous approaches have proposed methods to classify weather conditions into classes such as sunny and cloudy, their performance is still far from satisfactory. Under different weather conditions, we defined several categories of more robust weather features based on observations of outdoor images. We improve the classification accuracy using metric learning approaches. The results indicate that our method is able to provide much better performance than previous methods. The proposed method is also straightforward to implement and is computationally inexpensive, demonstrating the effectiveness of metric learning methods with computer vision problems.