Pyramid M-Shaped Network for Ordered Dithering Block Truncation Coding Image Restoration
H. P. Alim Wicaksono, Heri Prasetyo, Muhammad Farhan Ichlasul Amal, Jing-Ming Guo · 2020
This paper presents a novel deep learning-based technique for reconstructing the Ordered Dithering Block Truncation Coding (ODBTC) decoded image. The proposed method inherits the effectiveness of wavelets transform and Convolutional Neural Networks (CNN). The proposed technique employs the two-dimensional Decimated Wavelet Transform (DWT) to decompose the ODBTC image into low and high frequency sub-bands over various resolution. These image sub-bands are progressively reconstructed with the Pyramid M-Shaped CNN consisting multiple input and output. This scheme considers the ODBTC decoded image as noisy image in Which the information of image can reconstructed by modifying the wavelets sub-bands using residual learning strategy. The produced sub-bands can be combined and transformed back to improve the quality of ODBTC reconstructed image. As documented in experimental results, the proposed method yields promising result for the ODBTC image reconstruction.