Details

Autor(en) / Beteiligte

• Tomita, Keigo
• Tsutake, Chihiro
• Takahashi, Keita
• Fujii, Toshiaki

Titel

Denoising multi-view images by soft thresholding: A short-time DFT approach

Ist Teil von

• Signal processing. Image communication, 2022-07, Vol.105, p.116710, Article 116710

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• Short-time discrete Fourier transform (ST-DFT) is known as a promising technique for image and video denoising. The seminal work by Saito and Komatsu hypothesized that natural video sequences can be represented by sparse ST-DFT coefficients and noisy video sequences can be denoised on the basis of statistical modeling and shrinkage of the ST-DFT coefficients. Motivated by their theory, we develop an application of ST-DFT for denoising multi-view images. We first show that multi-view images have sparse ST-DFT coefficients as well and then propose a new statistical model, which we call the multi-block Laplacian model, based on the block-wise sparsity of ST-DFT coefficients. We finally utilize this model to carry out denoising by solving a convex optimization problem, referred to as the least absolute shrinkage and selection operator. A closed-form solution can be computed by soft thresholding, and the optimal threshold value is derived by minimizing the error function in the ST-DFT domain. We demonstrate through experiments the effectiveness of our denoising method compared with several previous denoising techniques. Our method implemented in Python language is available from https://github.com/ctsutake/mviden. •We address the problem of denoising multi-view images.•ST-DFT coefficients of multi-view images are modeled as Laplacian random variables.•Multi-view images are denoised by soft thresholding in the ST-DFT domain.•Threshold values are optimized based on Stein’s unbiased risk estimate.•The effectiveness of our method is discussed in terms of restoration quality.