Mixed Poisson-Gaussian noise reduction using a time-space fractional differential equations

Gholami Bahador F., Mokhtary P., Lakestanı M.

Information Sciences, cilt.647, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 647
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.ins.2023.119417
  • Dergi Adı: Information Sciences
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Library, Information Science & Technology Abstracts (LISTA), Metadex, MLA - Modern Language Association Database, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: Caputo derivative, CT images, Electron microscopy images, Fractional differential equations, Mixed Poisson-Gaussian noise, Noise reduction, Variable-order Grünwald-Letnikov derivative
  • Van Yüzüncü Yıl Üniversitesi Adresli: Evet

Özet

Images are frequently corrupted by various sorts of mixed or unrecognized noise, including mixed Poisson-Gaussian noise, rather than just a single kind of noise. In this work, we propose a time-space fractional differential equation to remove mixed Poisson-Gaussian noise. Combining fixed- and variable-order fractional derivatives allows us to maintain an image's high- and low-frequency components while eliminating noise. The current model, although primarily intended for mixed noise reduction, can indeed be utilized with great efficacy on images that have been solely degraded by Gaussian noise. In addition to this, a stable discretization strategy is presented. The illustrative results demonstrate that our scheme performs better than earlier models, reduces the staircase effect, and is applicable to electron microscopy and CT images.