Super resolution as an exciting application in image processing was studied widely
in the literature. This dissertation presents new approaches to video super resolution,
based on sparse coding and belief propagation. First, find candidate match
pixels on multiple frames using sparse coding and belief propagation. Second, incorporate
information from these candidate pixels with weights computed using the
Nonlocal-Means (NLM) method in the first approach or using SCoBeP method in
the second approach. The effectiveness of the proposed methods is demonstrated
for both synthetic and real video sequences in the experiment section. In addition,
the experimental results show that my models are naturally robust in handling super
resolution on video sequences affected by scene motions and/or small camera
motions.
Moreover, in this dissertation, I describe a denoising method using low-rank matrix
completion. In the proposed denoising approach, I present a patch-based video
denoising algorithm by grouping similar patches and then formulating the problem
of removing noise using a decomposition approach for low-rank matrix completion.
Experiments show that the proposed approach robustly removes mixed noise such
as impulsive noise, Poisson noise, and Gaussian noise from any natural noisy video.
Moreover, my approach outperforms state-of-the-art denoising techniques such as
VBM3D and 3DWTF in terms of both time and quality. My technique also achieves
significant improvement over time against other matrix completion methods
