Blocking artefacts reduction based on a ripple matrix permutation image of high‐frequency images in the wavelet domain

• Main Authors: Xiuli Du, Jinting Liu, Wei Zhang, Ya'na Lv
• Format: Article
• Language: English
• Published: Wiley 2021-08-01
• Series: IET Image Processing
• Online Access: https://doi.org/10.1049/ipr2.12217

Abstract Block compressed sensing (BCS) approaches based on matrix permutations effectively reduce blocking artefacts in the high‐quality reconstruction of images. To further reduce the blocking artefacts, the paper proposes a novel method for their processing in the wavelet domain based on the ripple matrix permutation (RMP) BCS approach. The method makes use of the energy distribution characteristics of images in the wavelet domain. The low‐frequency images contain the basic information. The high‐frequency images contain the edge textures information of the image, and these images are extremely sparse. Then, the proposed method performs matrix permutations only on the high‐frequency images. This avoids the obvious energy differences among the blocks. The method can better balance the textures among blocks; in turn, the blocking artefacts are reduced. The approach involves performing a wavelet decomposition on the image. Then, the transformed high‐frequency image is subjected to a RMP to achieve textures balancing. Finally, compressed sensing processing is performed on the permuted high‐frequency image. As a result, the balancing effect becomes more significant, and the low‐frequency part of the image remains unchanged, the differences among the blocks are reduced. Simulation results demonstrate that the high‐frequency part of the image wavelet domain is texture balanced. When the image is reconstructed after the compressed sensing step, the image quality is significantly improved.