Image super-resolution using focal stack and image fusion

Abstract

In this thesis, we propose two kinds of super-resolution algorithms using focal stack and image fusion technique. To begin with, we introduce focal stack based image super-resolution technique. Contrary to previous algorithms which use image misalignment to deduce ``missing`` details in a super-resolution image, we analyze the mutual information inherent in different defocus kernels to infer high resolution details that were missing in each of the individual low resolution out-of-focus image in a focal stack. As a by-product, this algorithm extends the depth-of-field to produce an all-in-focus super-resolution image. The effectiveness of this algorithm is demonstrated with quantitative analysis using synthetic examples and qualitative analysis using real-world examples. Moreover, we present another super-resolution method using image fusion technique called pan-sharpening that can produce high quality high-resolution multispectral image by fusing a high-resolution panchromatic image with a low resolution multispectral image. Unique in this approach are the color samples relocation algorithm and the optimization based edge aware interpolation method which protect the reconstructed images from aliasing and color diffusion artifacts around edge areas that are commonly arose in previous pan-sharpening algorithms. This approach is robust to misalignment errors between the high-resolution panchromatic image and the low resolution multispectral image. We evaluate our results quantitatively and qualitatively on both synthetic and real world satellite images. Comparing our results with results from previous methods, our results are sharper, aliasing-free, and with less color diffusion artifacts.