Paper Summary
"Spatio-temporal Super-Resolution Using Depth Map"
This paper describes a spatio-temporal super-resolution method using depth maps for static scenes. In the proposed method, the depth maps are used as the parameters to determine the corresponding pixels in multiple input images by assuming that intrinsic and extrinsic camera parameters are known. Because the proposed method can determine the corresponding pixels in multiple images by a one-dimensional search for the depth values without the planar assumption that is often used in the literature, spatial resolution can be increased even for complex scenes. In addition, since we can use multiple frames, temporal resolution can be increased even when large parts of the image are occluded in the adjacent frame. In experiments, the validity of the proposed method is demonstrated by generating spatio-temporal super-resolution images for both synthetic
and real movies.

Overview of the conference
SCIA is a conference in which much research about computer vision and pattern recognition is presented.
In this report, I introduce a paper "A New Triangulation-Base Method for Disparity Estimation in Image Sequences" as one of the greatest papers. In this research, initial disparity maps are obtained by triangulating a parse set of correspondences, detecting occlusions by a color distribution algorithm. The obtained approximations of the disparity maps are refined by a semi-global algorithm. I was very interested in this research because depth maps are used in our research and also applied to various research fields.

Opinion exchange
I could discuss our research with many researchers in the poster session.
In addition, I got to know students of GIST in Korea and talked about various research topics of image processing.