Abstract Multiscale image enhancement and representation is an important part of biological and machine early vision systems. The process of constructing this representation must be both rapid and insensitive to noise, while retaining image structure at all scales. This is a complex task as small scale structure is difficult to distinguish from noise, while larger scale structure requires more computational effort. In both cases, good localization can be problematic.
Errors can also arise when conflicting results at different scales require cross-scale arbitration.
Algorithms in the latter category typically use a set of variously sized blurring kernels to produce images, each of which retains structure at a different scale. The kernels used for the blurring are predefined and independent of the content of the image. Structure sensitive multiscale techniques attempt to analyze an image at a variety of scales within a single image. Klinger proposed the quad tree, one of the earliest structure-sensitive multiscale image representations. In this approach, a tree structure is built by recursively subdividing an image based on pixel variance in subregions.