Locust slice image is a kind of cartoon-like images, in which the texture possesses the property of self-similarity. Both of the texture and the noises belong to the high-frequency signals, and so it is difficult to tell the difference between them for most denoising methods. Aim to the problem, we propose a novel denoising method by combining the patch reordering with the shearlet transform. In the reordering process, the patches are divided into smooth and texture components. The filters obtained from the training set are employed to process the patches in smooth regions and the shearlet transform are employed to process the texture regions. The experiments show that the values of PSNR and SSIM of the processed images obtained by the proposed method are better than the common methods.

Shearlet and Patch Reordering Based Texture Preserving Denoising Method for Locust Slice Images

d'Amore M.;Villecco F.
2022

Abstract

Locust slice image is a kind of cartoon-like images, in which the texture possesses the property of self-similarity. Both of the texture and the noises belong to the high-frequency signals, and so it is difficult to tell the difference between them for most denoising methods. Aim to the problem, we propose a novel denoising method by combining the patch reordering with the shearlet transform. In the reordering process, the patches are divided into smooth and texture components. The filters obtained from the training set are employed to process the patches in smooth regions and the shearlet transform are employed to process the texture regions. The experiments show that the values of PSNR and SSIM of the processed images obtained by the proposed method are better than the common methods.
978-3-031-05229-3
978-3-031-05230-9
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4808894
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact