Abstract
Image denoising is a very important step in cryo-transmission electron microscopy (cryo-TEM) and the energy filtering TEM images before the 3D tomography reconstruction, as it addresses the problem of high noise in these images, that leads to a loss of the contained information. High noise levels contribute in particular to difficulties in the alignment required for 3D tomography reconstruction. This paper investigates the denoising of TEM images that are acquired with a very low exposure time, with the primary objectives of enhancing the quality of these low-exposure time TEM images and improving the alignment process. We propose denoising structures to combine multiple noisy copies of the TEM images. The structures are based on Bayesian estimation in the transform domains instead of the spatial domain to build a novel feature preserving image denoising structures; namely: wavelet domain, the contourlet transform domain and the contourlet transform with sharp frequency localization. Numerical image denoising experiments demonstrate the performance of the Bayesian approach in the contourlet transform domain in terms of improving the signal to noise ratio (SNR) and recovering fine details that may be hidden in the data. The SNR and the visual quality of the denoised images are considerably enhanced using these denoising structures that combine multiple noisy copies. The proposed methods also enable a reduction in the exposure time.
Highlights
Transmission electron microscopy (TEM) is used in the biological sciences to image biological samples and the resulting images can be used to visualize the molecular structure of proteins and large molecules
We propose new denoising algorithms that use the Bayesian denoiser proposed by Boubchir [7] in the contourlet transform domain and the contourlet with sharp frequency localization, so we take the advantages offered by both contourlet transform and Bayesian estimation to build a novel edge-preserving denoising structure
For the Bayesian denoiser in the contourlet transform and the contourlet-SD, we selected the number of levels for the DFB at each pyramidal level equal to (2, 3, 4, 5)
Summary
Transmission electron microscopy (TEM) is used in the biological sciences to image biological samples and the resulting images can be used to visualize the molecular structure of proteins and large molecules. The main objective of the denoising methods proposed in the literature is to reduce the noise as much as possible to achieve a good 3D image quality. Some denoising methods, such as Gaussian filtering techniques, succeed in eliminating noise, but decrease the image quality by blurring the edges. This paper investigates the denoising of TEM catalase images that are acquired with a very low exposure time and corrupted by additive Gaussian noise. This paper is organized as follows: Section 2 describes our proposed denoising methods for specific multiple noisy TEM images, namely catalase-crystals with different exposure times (e.g., different values of SNR).
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