Image and Depth Image Upsampling Using Adaptive Local Weighted Kernel
Date Issued
2015
Date
2015
Author(s)
Zhou, Shi-Han
Abstract
In this thesis, we study two topics about image processing, including depth image extraction and image upsampling. With the growing of technology, stereoscopic vision widely apply to image processing on multimedia and television. We find depth information of images by using stereo correspondence algorithms. The main idea of algorithms is matching feature points of two images, and then finding the position difference between every pixel, which is called disparity. Finally, the depth value can be obtained by the principle of stereo matching algorithm, and then we use these depth map information to conduct the application of view synthesis. Images and depth images upsampling are generally used in computer vision and 3D image processing. We propose a simple but effective upsampling method for automatically enhancing the image resolution, while preserving the essential structural information. The main idea is reconstructing images through the procedure of image recovering, that upsample low resolution to high resolution images quickly and get close to ground truth images. Different from the traditional upsampling technique, such as nearest interpolation, bicubic interpolation and etc. We proposed a method, called Adaptive Local Weighted Kernel, which can both apply to depth images and generic images. In undetermined upsampling images, unknown pixels between every original pixel will be interpolated by the convolution of low resolution image and Adaptive Local Weighted Kernel in the corresponding window size, thus this method can upsample images to high resolution rapidly. According to different image sources, the proposed algorithm will generate adaptive weighting to enhance edges, details and image quality of upsampling images respectively. On the other hand, the proposed algorithm can be applied to the new technique in image processing of television in the future, which can change image resolution from 2K(High Definition) to 4K (Quad Full High Definition) in high speed.
Subjects
depth image
disparity
view synthesis
image upsampling
nearest interpolation
bicubic interpolation
adaptive local weighted kernel
Type
thesis
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