  In many real-world applications, image data often come with noises,
corruptions or large errors. One approach to deal with noise image data is to
use data recovery techniques which aim to recover the true uncorrupted signals
from the observed noise images. In this paper, we first introduce a novel
corruption recovery transformation (CRT) model which aims to recover multiple
(or a collection of) corrupted images using a single affine transformation.
Then, we show that the introduced CRT can be efficiently constructed through
learning from training data. Once CRT is learned, we can recover the true
signals from the new incoming/test corrupted images explicitly. As an
application, we apply our CRT to image recognition task. Experimental results
on six image datasets demonstrate that the proposed CRT model is effective in
recovering noise image data and thus leads to better recognition results.
