Volume XLII-2/W12
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W12, 47-52, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W12-47-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W12, 47-52, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W12-47-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  09 May 2019

09 May 2019

DERMATOLOGICAL IMAGE DENOISING USING ADAPTIVE HENLM METHOD

A. Dogvanich1, N. Mamaev1, A. Krylov1, and N. Makhneva2 A. Dogvanich et al.
  • 1Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, 119991, Russia, Leninskie Gory, MSU BMK, Russia
  • 2Moscow Regional clinic of dermatology and venereology, Moscow, Russia

Keywords: image denoising, dermatology, optimal denoising parameters, no-reference image quality metrics, mutual information

Abstract. In this paper we propose automatic image denoising method based on Hermite functions (HeNLM). It is an extension of non-local means (NLM) algorithm. Differences between small image blocks (patches) are replaced by differences between feature vectors thus reducing computational complexity. The features are calculated in coordinate system connected with image gradient and are invariant to patch rotation. HeNLM method depends on the parameter that controls filtering strength. To chose automatically this parameter we use a no-reference denoising quality assessment method. It is based on Hessian matrix analysis. We compare the proposed method with full-reference methods using PSNR metrics, SSIM metrics, and its modifications MSSIM and CMSC. Image databases TID, DRIVE, BSD, and a set of dermatological immunofluorescence microscopy images were used for the tests. It was found that more perceptual CMSC and MSSIM metrics give worse correspondence than SSIM and PSNR to the results of information preservation by the non-reference image denoising.