scholarly journals Computer Enhancement of Weak-Beam Images

1973 ◽  
Vol 31 ◽  
pp. 274-275
Author(s):  
H.M. Horgen ◽  
R. E. Villagrana ◽  
D. M. Maher
Keyword(s):  
Signal To Noise Ratio ◽  
Image Contrast ◽  
Signal To Noise ◽  
Low Contrast ◽  
Weak Beam ◽  
Quantitative Image ◽  
Image Width ◽  
Contrast Analysis ◽  
Noise Ratio ◽  
The Individual

In order to perform quantitative image-contrast analysis of low-contrast electron micrographs one must first increase the signal to noise ratio. As an example, consider the weak-beam method of imaging defects by transmission electron microscopy. In this technique an increase in the resolution of closely spaced dislocations is obtained through a narrowing of the individual dislocation image widths. However, this reduction in image width is accompanied by a corresponding decrease in the signal to noise ratio, which in many instances renders quantitative image-contrast analysis impractical. In this note we present an example of the computer image enhancement of a weak-beam micrograph.

scholarly journals Evaluation of sted super-resolution image quality by image correlation spectroscopy (QuICS)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elena Cerutti ◽  
Morgana D’Amico ◽  
Isotta Cainero ◽  
Gaetano Ivan Dellino ◽  
Mario Faretta ◽  
...  
Keyword(s):  
Autocorrelation Function ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Image Contrast ◽  
Correlation Spectroscopy ◽  
Image Correlation ◽  
Signal To Noise ◽  
Image Correlation Spectroscopy ◽  
Noise Ratio

AbstractQuantifying the imaging performances in an unbiased way is of outmost importance in super-resolution microscopy. Here, we describe an algorithm based on image correlation spectroscopy (ICS) that can be used to assess the quality of super-resolution images. The algorithm is based on the calculation of an autocorrelation function and provides three different parameters: the width of the autocorrelation function, related to the spatial resolution; the brightness, related to the image contrast; the relative noise variance, related to the signal-to-noise ratio of the image. We use this algorithm to evaluate the quality of stimulated emission depletion (STED) images of DNA replication foci in U937 cells acquired under different imaging conditions. Increasing the STED depletion power improves the resolution but may reduce the image contrast. Increasing the number of line averages improves the signal-to-noise ratio but facilitates the onset of photobleaching and subsequent reduction of the image contrast. Finally, we evaluate the performances of two different separation of photons by lifetime tuning (SPLIT) approaches: the method of tunable STED depletion power and the commercially available Leica Tau-STED. We find that SPLIT provides an efficient way to improve the resolution and contrast in STED microscopy.

scholarly journals Estimation technique of global fringe visibility using in interferometers with adjustable visibility

2020 ◽  
Author(s):  
fu-zhong bai ◽  
Jiayi Chen ◽  
Xiaojuan Gao ◽  
Yongxiang Xu
Keyword(s):  
Theoretical Model ◽  
Fringe Pattern ◽  
Signal To Noise Ratio ◽  
Estimation Technique ◽  
Signal To Noise ◽  
Fringe Visibility ◽  
Low Contrast ◽  
Accuracy Measurement ◽  
Noise Ratio

Abstract In the accuracy measurement of phase from interferometers with adjustable fringe visibility, it needs to estimate the visibility of experimental patterns so as to obtain the interference patterns with the maximum visibility. We develop the Fourier-polar transform and combine the directional projection to estimate the global visibility of carrier fringe pattern. The technique is especially used for low-quality fringe pattern such as low contrast and low (signal to noise ratio) SNR that often appear in the interferometric experiment. An illustrative experiment based on the radial shearing interferometer is given. Results generated from this technique are compared with the derived values from theoretical model, and exemplary agreement between both is demonstrated.

VERTICAL STACKING AS A REFLECTION FILTER

Geophysics ◽  
1977 ◽  
Vol 42 (5) ◽  
pp. 1045-1047
Author(s):  
Franklyn K. Levin
Keyword(s):  
Seismic Reflection ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Noise Ratio ◽  
The Individual

In order to improve the signal‐to‐noise ratio of noisy seismic reflection data before velocity determination by computer, it’s common practice to sum several traces having the same source‐to‐geophone (SGD) separation but different CDP points. We say we form a vertical stack. When reflectors are horizontal planes, vertical stacking simply reduces the noise without distorting the reflection waveforms. However, when a reflector is a dipping plane, the individual reflections from that plane may be identical; but they arrive at slightly different times. As a result, the sum reflection is distorted: vertical stacking acts as a filter.

scholarly journals MR-contrast-aware image-to-image translations with generative adversarial networks

2021 ◽  
Author(s):  
Jonas Denck ◽  
Jens Guehring ◽  
Andreas Maier ◽  
Eva Rothgang
Keyword(s):  
Signal To Noise Ratio ◽  
Image Contrast ◽  
Clinical Indication ◽  
Generative Adversarial Networks ◽  
Acquisition Time ◽  
Large Set ◽  
Mr Images ◽  
Signal To Noise ◽  
Style Transfer ◽  
Noise Ratio

Abstract Purpose A magnetic resonance imaging (MRI) exam typically consists of several sequences that yield different image contrasts. Each sequence is parameterized through multiple acquisition parameters that influence image contrast, signal-to-noise ratio, acquisition time, and/or resolution. Depending on the clinical indication, different contrasts are required by the radiologist to make a diagnosis. As MR sequence acquisition is time consuming and acquired images may be corrupted due to motion, a method to synthesize MR images with adjustable contrast properties is required. Methods Therefore, we trained an image-to-image generative adversarial network conditioned on the MR acquisition parameters repetition time and echo time. Our approach is motivated by style transfer networks, whereas the “style” for an image is explicitly given in our case, as it is determined by the MR acquisition parameters our network is conditioned on. Results This enables us to synthesize MR images with adjustable image contrast. We evaluated our approach on the fastMRI dataset, a large set of publicly available MR knee images, and show that our method outperforms a benchmark pix2pix approach in the translation of non-fat-saturated MR images to fat-saturated images. Our approach yields a peak signal-to-noise ratio and structural similarity of 24.48 and 0.66, surpassing the pix2pix benchmark model significantly. Conclusion Our model is the first that enables fine-tuned contrast synthesis, which can be used to synthesize missing MR-contrasts or as a data augmentation technique for AI training in MRI. It can also be used as basis for other image-to-image translation tasks within medical imaging, e.g., to enhance intermodality translation (MRI → CT) or 7 T image synthesis from 3 T MR images.

scholarly journals Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks

IUCrJ ◽  
2020 ◽  
Vol 7 (6) ◽  
pp. 1142-1150
Author(s):  
Eugene Palovcak ◽  
Daniel Asarnow ◽  
Melody G. Campbell ◽  
Zanlin Yu ◽  
Yifan Cheng
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Convolutional Neural Networks ◽  
Signal To Noise Ratio ◽  
Image Contrast ◽  
Signal To Noise ◽  
Pass Filter ◽  
Processing Pipeline ◽  
Low Pass ◽  
Noise Ratio

In cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image-processing pipeline. Classic methods improve low-frequency image contrast experimentally, by imaging with high defocus, or computationally, by applying various types of low-pass filter. These contrast improvements typically come at the expense of the high-frequency SNR, which is suppressed by high-defocus imaging and removed by low-pass filtration. Recently, convolutional neural networks (CNNs) trained to denoise cryo-EM images have produced impressive gains in image contrast, but it is not clear how these algorithms affect the information content of the image. Here, a denoising CNN for cryo-EM images was implemented and a quantitative evaluation of SNR enhancement, induced bias and the effects of denoising on image processing and three-dimensional reconstructions was performed. The study suggests that besides improving the visual contrast of cryo-EM images, the enhanced SNR of denoised images may be used in other parts of the image-processing pipeline, such as classification and 3D alignment. These results lay the groundwork for the use of denoising CNNs in the cryo-EM image-processing pipeline beyond particle picking.

Entropy based Range Optimized Brightness Preserved Histogram-Equalization for Image Contrast Enhancement

2016 ◽  
Vol 6 (1) ◽  
pp. 59-72 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Sankhadip Sen ◽  
Kaustav Sarkar ◽  
Sanjoy Das
Keyword(s):  
Contrast Enhancement ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Image Contrast ◽  
Experimental Results ◽  
Signal To Noise ◽  
Image Histogram ◽  
Image Contrast Enhancement ◽  
Noise Ratio

In this study the over-enhancement problem of traditional Histogram-Equalization (HE) has been removed to some extent by a variant of HE called Range Optimized Entropy based Bi-Histogram Equalization (ROEBHE). In ROEBHE image histogram has been thresholded into two sub-histograms i.e. histograms corresponding to background and foreground. The threshold is calculated by maximizing the sum of the entropy of these two sub-histograms. The range for equalization has been optimized by maximizing the Peak-Signal to Noise ratio (PSNR). The experimental results prove that ROEBHE has prevailed over existing methods and PSNR is a better range optimizer than Absolute Mean Brightness Error (AMBE).

scholarly journals Proposal of a New Index Based on Signal-to-Noise Ratio for Low-contrast Detectability in Computed Tomographic Imaging

2017 ◽  
Vol 73 (7) ◽  
pp. 537-547
Author(s):  
Junichi Hashimoto ◽  
Shinji Abe ◽  
Yoshiyuki Ishimori ◽  
Masahiko Monma ◽  
Akio Tsumuraya ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Tomographic Imaging ◽  
Signal To Noise ◽  
Low Contrast ◽  
Computed Tomographic ◽  
Noise Ratio ◽  
Computed Tomographic Imaging

Optimization of Local Contrast Factor with Adaptive Brightness Improvement: Impact on Mammogram Image Analysis

2021 ◽  
Vol 11 (8) ◽  
pp. 2153-2166
Author(s):  
Nurshafira Hazim Chan ◽  
Khairunnisa Hasikin ◽  
Nahrizul Adib Kadri ◽  
Mokhzaini Azizan ◽  
Muzammil B. Jusoh
Keyword(s):  
Early Stage ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Local Contrast ◽  
Signal To Noise ◽  
Low Contrast ◽  
Background Removal ◽  
Contrast Factor ◽  
Noise Ratio

Mammography has been known worldwide as the most common imaging modalities utilized for early detection of breast cancer. The mammographic images produced are in greyscale, however they often produced low contrast images, contain artefacts and noise, as well as non-uniform illumination. These limitations can be overcame in the pre-processing stage with the image enhancement process. Therefore, in this research we developed an optimized enhancement framework where the local contrast factor is manipulated to preserve details of the image. This method aims to improve the overall image visibility without altering histogram of the original image, which will affect the segmentation and classification processes. We performed dark background removal in the image histogram at early stage to increase the efficiency of new mean histogram calculation. Then, the histogram is separated into two partitions to allow histogram clipping process to be conducted individually for underexposed and overexposed areas. Consequently, the local contrast factor optimization is conducted to preserve the image details. The results from our proposed method are compared with other methods by the measurement of peak signal-to-noise ratio, structural similarity index, average contrast, and average entropy difference. The results portrayed that our proposed method yield better quality over the others with highest peak signal-to-noise ratio of 32.676. In addition, in terms of qualitative analysis, our proposed method depicted better lesion segmentation with smoother shape of the lesion.

scholarly journals EVALUATION OF STED SUPER-RESOLUTION IMAGE QUALITY BY IMAGE CORRELATION SPECTROSCOPY (QuICS)

2021 ◽  
Author(s):  
Elena Cerutti ◽  
Morgana D'Amico ◽  
Isotta Cainero ◽  
Gaetano Ivan Dellino ◽  
Mario Faretta ◽  
...  
Keyword(s):  
Autocorrelation Function ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Image Contrast ◽  
Correlation Spectroscopy ◽  
Image Correlation ◽  
Signal To Noise ◽  
Image Correlation Spectroscopy ◽  
Noise Ratio

Quantifying the imaging performances in an unbiased way is of outmost importance in super-resolution microscopy. Here, we describe an algorithm based on image correlation spectroscopy (ICS) that can be used to assess the quality of super-resolution images. The algorithm is based on the calculation of an autocorrelation function and provides three different parameters: the width of the autocorrelation function, related to the spatial resolution; the brightness, related to the image contrast; the relative noise variance, related to the signal-to-noise ratio of the image. We use this algorithm to evaluate the quality of stimulated emission depletion (STED) images of DNA replication foci in U937 cells acquired under different imaging conditions. Increasing the STED power improves the resolution but may reduce the image contrast. Increasing the number of line averages improves the signal-to-noise ratio but facilitates the onset of photobleaching and subsequent reduction of the image contrast. Finally, we evaluate the performances of two different separation of photons by lifetime tuning (SPLIT) approaches: the method of tunable STED power and the commercially available Leica Tau-STED. We find that SPLIT provides an efficient way to improve the resolution and contrast in STED microscopy.

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