Image interpolation using texture orientation map and kernel Fisher discriminant

Xiaolin Wu, Xiangjun Zhang

Abstract: We propose a new non-linear approach of high order context to image interpolation. A global texture orientation map is generated by directional Gabor filters to estimate the edge directions in subpixel precision. The interpolation direction is further refined by a kernel Fisher discriminant that exploits prior knowledge gained from a training set. Experiments show that the proposed method can preserve edge sharpness and subdue the ringing artifacts better than the existing methods, and it obtains higher PSNR as well.