An adaptive super-resolution of videos with noise information on camera systems
Toshiyuki Ono, Hiroshi Hasegawa, Isao Yamada, K. Sakaniwa · 2006
We present a novel adaptive super-resolution of videos based on an embedded constraint version of adaptive projected subgradient method (Yamada & Ogura, Numerical Functional Analysis and Optimization, vol 25, no.7&8, p.593-617, 2004). The super-resolution image recovery problem is formulated as an estimation of linear time-varying systems, which is a modified version of (Elad & Feuer, IEEE Trans. on Image Proc., vol.8, no.3, p.387-395, 1999). Our method efficiently improves the estimation accuracy by simple iterative operations which can be processed on parallel systems. Robustness to additive noise as well as inaccurate estimation of degradation parameters, is realized by incorporating stochastic information of the noise.