Multiple Images Recovery Using a Single Affine Transformation
This addresses image data corruption in real-world applications, but it appears incremental as it builds on existing recovery techniques with a specific transformation approach.
The paper tackles the problem of recovering multiple corrupted images by introducing a novel corruption recovery transformation (CRT) model that uses a single affine transformation, and experimental results on six datasets show it effectively recovers noise and improves recognition.
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.