Design of Image Matched Non-Separable Wavelet using Convolutional Neural Network
This work addresses the need for customized wavelets in image processing tasks, though it appears incremental as it applies existing CNN techniques to a specific wavelet design problem.
The paper tackled the problem of designing image-matched non-separable wavelets for applications like image classification and segmentation by proposing a novel method using a convolutional neural network (CNN) on a quincunx lattice, achieving perfect reconstruction after training as demonstrated in simulations on standard images.
Image-matched nonseparable wavelets can find potential use in many applications including image classification, segmen- tation, compressive sensing, etc. This paper proposes a novel design methodology that utilizes convolutional neural net- work (CNN) to design two-channel non-separable wavelet matched to a given image. The design is proposed on quin- cunx lattice. The loss function of the convolutional neural network is setup with total squared error between the given input image to CNN and the reconstructed image at the output of CNN, leading to perfect reconstruction at the end of train- ing. Simulation results have been shown on some standard images.