Super-Resolution Using Sub-band Constrained Total Variation
Published in International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), 2007
Recommended citation: P. Chatterjee, V.P. Namboodiri, S. Chaudhuri (2007) “Super-Resolution Using Sub-band Constrained Total Variation” In: Sgallari F., Murli A., Paragios N. (eds) Scale Space and Variational Methods in Computer Vision SSVM 2007. Lecture Notes in Computer Science, vol 4485. Springer, Berlin, Heidelberg http://vinaypn.github.io/files/ssvm07.pdf
Super-resolution of a single image is a severely ill-posed problem in computer vision. It is possible to consider solving this problem by considering a total variation based regularization framework. The choice of total variation based regularization helps in formulating an edge preserving scheme for super-resolution. However, this scheme tends to result in a piece-wise constant resultant image. To address this issue, we extend the formulation by incorporating an appropriate sub-band constraint which ensures the preservation of textural details in trade off with noise present in the observation. The proposed framework is extensively evaluated and the experimental results for the same are presented
Recommended citation: P. Chatterjee, V.P. Namboodiri, S. Chaudhuri (2007) “Super-Resolution Using Sub-band Constrained Total Variation” In: Sgallari F., Murli A., Paragios N. (eds) Scale Space and Variational Methods in Computer Vision SSVM 2007. Lecture Notes in Computer Science, vol 4485. Springer, Berlin, Heidelberg