An FIR image interpolation filter design method based on properties of human vision

Image interpolation is an important image operation. It is commonly used in image enlargement to obtain a close-up view of the detail of an image. From sampling theory, an ideal low-pass filter can be used for image interpolation. However, ripples appear around image edges which are annoying to a human viewer. The authors introduce a new FIR image interpolation filter known as a perceptually weighted least square (PWLS) filter which is designed using both sampling theory and properties of human vision. The goal of this design approach is to minimize the ripple response around edges of the interpolated images and to best satisfy frequency response constraints. The interpolation results using the proposed approach are substantially better than those resulting from replication or bilinear interpolation, and are at least as good as and possibly better than that of cubic convolution interpolation