A Two-layer Constraint Image Interpolation Model Basing on Both Local and Global Information
双层约束下基于局部和全局信息的图像插值新模型

In this paper, a novel two-layer constraint image interpolation model is proposed. The novel model interpolates image under the constraint of gradient magnitude in original un-interpolated image. The edge pixel is processed by Partial Differential Equation (PDE). PDE enhances edge and smoothes jaggies in the edge. The pixel in smooth regions of under-interpolated image is processed by Non Local (NL)-means model. NL-means model gets gray value of pixel by weighted averaging global information in image and image is smoothed. Texture image interpolated by the novel model preserves the entire texture pattern; the jaggies in linear structure of texture is smoothed too. It is even proved by theory and experiments that noisy image can be interpolated directly to the required size using this novel model.