Reducing molecular simulation time for AFM images based on super-resolution methods

• Zhipeng Dou,
• Jianqiang Qian,
• Yingzi Li,
• Rui Lin,
• Jianhai Wang,
• Peng Cheng and
• Zeyu Xu

Beilstein J. Nanotechnol. 2021, 12, 775–785, doi:10.3762/bjnano.12.61

• usually used to determine the image quality loss after image compression, denoising, and reconstruction. The SSIM is a measure of the similarity between two images. From Figure 6 we could find that with an increase in the undersampling rate, the reconstruction quality gradually improves. The reconstructed

A novel method to remove impulse noise from atomic force microscopy images based on Bayesian compressed sensing

• Yingxu Zhang,
• Yingzi Li,
• Zihang Song,
• Zhenyu Wang,
• Jianqiang Qian and
• Junen Yao

Beilstein J. Nanotechnol. 2019, 10, 2346–2356, doi:10.3762/bjnano.10.225

• , China School of Physics, Beihang University, Beijing 100191, China 10.3762/bjnano.10.225 Abstract A novel method based on Bayesian compressed sensing is proposed to remove impulse noise from atomic force microscopy (AFM) images. The image denoising problem is transformed into a compressed sensing

• reconstructed separately in the proposed method, which will not reduce the quality of the reconstructed image. The denoising experiments are conducted to demonstrate that the proposed method can remove the impulse noise from AFM images while preserving the details of the image. Compared with other methods, the

• proposed method is robust and its performance is not influenced by the noise density in a certain range. Keywords: atomic force microscopy (AFM); Bayesian compressed sensing; denoising; image processing; impulse noise; Introduction Atomic force microscopy (AFM) is a powerful tool in the fields of

Data-adaptive image-denoising for detecting and quantifying nanoparticle entry in mucosal tissues through intravital 2-photon microscopy

• Torsten Bölke,
• Lisa Krapf,
• Regina Orzekowsky-Schroeder,
• Tobias Vossmeyer,
• Jelena Dimitrijevic,
• Horst Weller,
• Anna Schüth,
• Antje Klinger,
• Gereon Hüttmann and
• Andreas Gebert

Beilstein J. Nanotechnol. 2014, 5, 2016–2025, doi:10.3762/bjnano.5.210

• per pixel, it cannot be avoided by technical means. Fluorescent nanoparticles contained in the tissue may be represented by a few bright pixels which closely resemble the noise structure. We here present a data-adaptive method for digital denoising of datasets obtained by 2-photon microscopy. The

• microscopy (2PM); denoising; in vivo imaging; nanoparticles; signal to noise ratio (SNR); quantum dots; Introduction Imaging methods applied to detect fluorescent nanoparticles in mucosal tissues should provide high optical resolution and allow large volumes to be scanned. An important and versatile tool

• , raw image data are digitally processed to reconstruct an estimation of the underlying ground-truth signal by suppressing the noise. This so-called denoising is a typical task in signal processing, for which, in the last decades, numerous methods were developed [5][6][7][8][9][10]. A few modern