Multi-spectral missing label prediction via restoration using deep residual dictionary learning

Dictionary learning (DL) is one of the popular sparse coding machine learning techniques. In image processing literature, every input image is represented as the sparse linear combination of basis vectors. DL has been shown to have wide applications for image restoration as well as pattern recognition problems. In DL, the input image is factorized into dictionary and sparse codes. This factorization always leaves a residual or approximation error. Very few works in the literature had focused on to leverage the information present in this residual. In this paper, we use residuals within our framework and show that the restoration performance or accurate prediction of missing label in multi-spectral images can be significantly improved over conventional DL based techniques. We initially show that the higher order frequencies are propagated through residuals. Then we show that incorporating this residual in the image restoration methodology can significantly improve the outcomes. Finally, we propose a technique to solve the problem of missing label prediction by using a restoration based deep residual dictionary learning framework.