If a blurred image is used as the degenerate image, interpolation attenuates high frequencies to varying degrees, and extrapolation boosts them, sharpening the image by unsharp masking. Varying alpha acts as a kernel scale factor, so a series of convolutions differing only in scale can be done easily, independent of the size of the kernel. Since blurring, unlike sharpening, is often a separable operation, sharpening by extrapolation may be far more efficient for large kernels. Note that global contrast control, local contrast control, and sharpening form a continuum. Global contrast pushes pixel components towards or away from the average image luminance.
Local contrast is similar, but uses local area luminance.
Unsharp masking is the extreme case, using only the color of nearby pixels. Here sharpness, tint, and saturation are all altered. Conclusion Image applications frequently need to produce multiple degrees of manipulation interactively. Image applications frequently need to interactively manipulate an image by continuously changing a single parameter. Interpolation and extrapolation of images can be a unifying approach, providing a single function that supports many common image processing operations.