CNN image compression and reconstruction based on non-orthogonal wavelet transform

In practical image processing by wavelet transform (WT), the function orthogonality is required for reconstruction of the original image. The orthogonality has disadvantage that the selected filter is not necessarily optimal from a viewpoint of human retinal realization. It is not necessary to select an orthogonal template in cellular neural network (CNN) image processing, because the CNN is nonlinear analog circuit to obtain equilibrium points automatically and simultaneously. This paper describes CNN image compression and reconstruction based on a nonorthogonal WT. This system have an advantage of nondependency of image scanning by spatio-temporal CNN dynamics. It is very important that the reconstruction of transmitted compression image is done simultaneously by parallel neurons based on the “regularization” of ill-posed problem which is caused in a retinal system of a human brain