THESIS
2016
ix, 1, 38 pages : illustrations ; 30 cm
Abstract
The goal of video super-resolution (VSR) is to generate the high-resolution
(HR) sequence based on the low-resolution (LR) input. In the past several
decades, multi-image super-resolution (MISR) methods have dominated VSR.
However, MISR, which generates each HR frame independently, does not consider
the temporal correlations among reconstructed HR frames, causing the artifact
in the visual consistency. In the meantime, traditional MISR methods
cannot handle situations with complex motions because of requiring highly accurate
motion estimation. In our work, we propose a sequential model, Bidirectional
Convolutional Long Short Term Memory (Bi-ConvLSTM), to explore temporal
dependencies and spatial coherence through forward and backward directions for
VSR. We further take each in...[
Read more ]
The goal of video super-resolution (VSR) is to generate the high-resolution
(HR) sequence based on the low-resolution (LR) input. In the past several
decades, multi-image super-resolution (MISR) methods have dominated VSR.
However, MISR, which generates each HR frame independently, does not consider
the temporal correlations among reconstructed HR frames, causing the artifact
in the visual consistency. In the meantime, traditional MISR methods
cannot handle situations with complex motions because of requiring highly accurate
motion estimation. In our work, we propose a sequential model, Bidirectional
Convolutional Long Short Term Memory (Bi-ConvLSTM), to explore temporal
dependencies and spatial coherence through forward and backward directions for
VSR. We further take each input and output of Bi-ConvLSTM as two successive
LR and HR frames to avoid motion estimation and preserve output temporal
correlations. Taking advantage of the temporal information provided in the successive
frames through Bi-ConvLSTM makes the output temporally coherent and
better visualized. To further improve the VSR performance, we propose a novel
adaptation framework which utilizes test data's self-information for specic model
refinement. Experiments illustrate that our Bi-ConvLSTM outperforms the state-of-
the-art VSR methods and the adaptation framework can further enhance the
VSR performance.
Post a Comment