Image Pair Analysis With Matrix-Value Operator

Image pair analysis provides significant image pair priori which describes the dependency between training image pairs for various learning-based image processing. For avoiding the information loss caused by vectorizing training images, a novel matrix-value operator learning method is proposed for image pair analysis. Sample-dependent operators, named image pair operators (IPOs) by us, are employed to represent the local image-to-image dependency defined by each of the training image pairs. A linear combination of IPOs is learned via operator regression for representing the global dependency between input and output images defined by all of the training image pairs. The proposed operator learning method enjoys the image-level information of training image pairs because IPOs enable training images to be used without vectorizing during the learning and testing process. By applying the proposed algorithm in learning-based super-resolution, the efficiency and the effectiveness of the proposed algorithm in learning image pair information is verified by experimental results.