scholarly journals A method for presenting biomedical images based on a model of receptive fields of human visual perception

2021 ◽  
Vol 2091 (1) ◽  
pp. 012027
Author(s):  
V E Antsiperov ◽  
V A Kershner
Keyword(s):  
Image Processing ◽  
Visual Perception ◽  
Bayesian Learning ◽  
Signal To Noise Ratio ◽  
Receptive Fields ◽  
New Method ◽  
Human Visual Perception ◽  
Signal To Noise ◽  
Biomedical Images ◽  
Local Characteristics

Abstract The paper is devoted to the development of a new method for presenting biomedical images based on local characteristics of the intensity of their shape. The proposed method of image processing is focused on images that have low indicators of the intensity of the recorded radiation, resolution, contrast and signal-to-noise ratio. The method is based on the principles of machine (Bayesian) learning and on samples of random photo reports. This paper presents the results of the method and its connection with modern approaches in the field of image processing.

A system for fast digital image processing of asynchronous SEM signals

1988 ◽  
Vol 46 ◽  
pp. 676-677
Author(s):  
Evariste F. Osten ◽  
John C. Schultz
Keyword(s):  
Image Processing ◽  
Visual Perception ◽  
Visual Information ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Color Processing ◽  
Video Signals ◽  
Examination Time ◽  
Time Required ◽  
Digital Frame

The time required to examine a specimen's features with an SEM before photographically recording representative images is related to the amount of visual information about that specimen that is available from the SEM's viewing CRT. In a laboratory that examines several thousand specimens each year, many in low signal-to-noise situations, the accumulated examination time can be significant. Image processing to increase the information content of the viewed image can reduce the time needed to examine the specimen. Digital frame integration can be used to improve an image's signal-to-noise ratio and color processing of the observed image can be used to provide enhanced visual perception. Using a passive interface with the SEM for image processing has the advantage that it doesn't interfere with the SEM scan electronics nor does it affect normal SEM operation. A difficulty in image processing arises when using asynchronous SEM signals - video signals that lack synch pulses and therefore do not conform to standard RS-170 video.

scholarly journals Review About Various Satellite Image Segmentation

2018 ◽  
Vol 9 (3) ◽  
pp. 633
Author(s):  
Shabir Ahmed Mir ◽  
T. Padma
Keyword(s):  
Image Segmentation ◽  
Visual Perception ◽  
Machine Vision ◽  
Satellite Images ◽  
Semantic Information ◽  
Signal To Noise Ratio ◽  
Satellite Image ◽  
Human Visual Perception ◽  
Signal To Noise ◽  
Prior Probabilities

<p>In this paper, a review about different algorithm is proposed efficiently to segment the satellite images. Segmentation of Image is one of the promising and active researches in recent years. As literature prove that region segmentation will produce better results. Human visual perception is more effective than any machine vision systems for extracting semantic information from image. There are various segmentation techniques are available. Fuzzy C Means (FCM), Expectation Minimization (EM) and K-Means algorithm is developed to estimate parameters of the prior probabilities and likelihood probabilities. Finally Peak Signal to Noise Ratio (PSNR) is calculated for all the algorithms and reviewed.</p>

Retinal Digital Image Quality Improvement as A Diabetes Retinopatic Disease Detection Effort

2020 ◽  
Vol 4 (2) ◽  
pp. 53-60
Author(s):  
Latifah Listyalina ◽  
Yudianingsih Yudianingsih ◽  
Dhimas Arief Dharmawan
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Image Quality ◽  
Digital Image ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Vital Role ◽  
Retinal Images ◽  
Signal To Noise ◽  
Noise Ratio

Image processing is a technical term useful for modifying images in various ways. In medicine, image processing has a vital role. One example of images in the medical world, namely retinal images, can be obtained from a fundus camera. The retina image is useful in the detection of diabetic retinopathy. In general, direct observation of diabetic retinopathy is conducted by a doctor on the retinal image. The weakness of this method is the slow handling of the disease. For this reason, a computer system is required to help doctors detect diabetes retinopathy quickly and accurately. This system involves a series of digital image processing techniques that can process retinal images into good quality images. In this research, a method to improve the quality of retinal images was designed by comparing the methods for adjusting histogram equalization, contrast stretching, and increasing brightness. The performance of the three methods was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Signal to Noise Ratio (SNR). Low MSE values and high PSNR and SNR values indicated that the image had good quality. The results of the study revealed that the image was the best to use, as evidenced by the lowest MSE values and the highest SNR and PSNR values compared to other techniques. It indicated that adaptive histogram equalization techniques could improve image quality while maintaining its information.

scholarly journals Simulation of the Photodetection in the PPS CMOS Sensors

2013 ◽  
Vol 8-9 ◽  
pp. 611-618
Author(s):  
Florin Toadere ◽  
Radu Arsinte
Keyword(s):  
Image Processing ◽  
Input Data ◽  
Signal To Noise Ratio ◽  
Image Processing Algorithm ◽  
Signal To Noise ◽  
Spectral Image ◽  
Image Capture ◽  
Visible Image ◽  
Optical Part ◽  
Electrical Part

The paper contains an analysis and simulation of passive pixel based sensors. The passive pixel CMOS image acquisition sensor (PPS) is the key part of a visible image capture systems. The PPS is a complex circuit composed by an optical part and an electrical part, both analog and digital. The goal of this paper is to simulate the functionality of the photodetection process that happens in the PPS sensor. The photodetector is responsible with the conversion from photons to electrical charges and then into current. In the optical part, the sensor is analyzed by a spectral image processing algorithm which uses as input data: the lenses array transmittance, the red, green and blue filters and the quantum efficiency of the PPS. In the electrical part of simulation, the program is computing the signal to noise ratio of the sensor taking into account the photon shot, white and fixed pattern noises. Our basic analysis is based on camera equation to which we add the noises.

Estimating Optimum Filter Length in Linear Prediction

1997 ◽  
Vol 51 (5) ◽  
pp. 718-720 ◽  
Author(s):  
O.-P. Sievänen
Keyword(s):  
Raman Spectra ◽  
Prediction Error ◽  
Linear Prediction ◽  
Signal To Noise Ratio ◽  
New Method ◽  
Signal To Noise ◽  
Filter Length ◽  
Optimum Filter ◽  
Good Signal ◽  
Prediction Filter

In this article a new method to estimate optimum filter length in linear prediction is described. Linear prediction was used to enhance resolution of a spectrum. In particular, the dependence of prediction error on filter length has been studied. With calculations of simulated spectra it is shown that the prediction error falls rapidly when the filter length attains its optimum value. This effect is quite pronounced when the spectrum has a good signal-to-noise ratio and the modified covariance method is used to calculate prediction filter coefficients. The method is illustrated with applications to real Raman spectra.

What visual perception model is optimal in terms of signal-to-noise ratio?

2000 ◽  
Author(s):  
Yury E. Shelepin ◽  
Nikolay N. Krasilnikov ◽  
Olga I. Krasilnikova ◽  
Valery N. Chihman
Keyword(s):  
Visual Perception ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Perception Model ◽  
Noise Ratio

ROBUST SVD FILTERING FOR LOW SIGNAL-TO-NOISE RATIO SEISMIC DATA

Geophysics ◽  
2021 ◽  
pp. 1-51
Author(s):  
Chao Wang ◽  
Yun Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Singular Values ◽  
New Method ◽  
Signal To Noise ◽  
Coherent Noise ◽  
Edge Preserving ◽  
Singular Vectors ◽  
Reduced Rank ◽  
Noise Ratio

Reduced-rank filtering is a common method for attenuating noise in seismic data. As conventional reduced-rank filtering distinguishes signals from noises only according to singular values, it performs poorly when the signal-to-noise ratio is very low, or when data contain high levels of isolate or coherent noise. Therefore, we developed a novel and robust reduced-rank filtering based on the singular value decomposition in the time-space domain. In this method, noise is recognized and attenuated according to the characteristics of both singular values and singular vectors. The left and right singular vectors corresponding to large singular values are selected firstly. Then, the right singular vectors are classified into different categories according to their curve characteristics, such as jump, pulse, and smooth. Each kind of right singular vector is related to a type of noise or seismic event, and is corrected by using a different filtering technology, such as mean filtering, edge-preserving smoothing or edge-preserving median filtering. The left singular vectors are also corrected by using the filtering methods based on frequency attributes like main-frequency and frequency bandwidth. To process seismic data containing a variety of events, local data are extracted along the local dip of event. The optimal local dip is identified according to the singular values and singular vectors of the data matrices that are extracted along different trial directions. This new filtering method has been applied to synthetic and field seismic data, and its performance is compared with that of several conventional filtering methods. The results indicate that the new method is more robust for data with a low signal-to-noise ratio, strong isolate noise, or coherent noise. The new method also overcomes the difficulties associated with selecting an optimal rank.

A Hybrid Image Processing Approach to Enhance Signal to Noise Ratio of Carbonate Micro CT-Images

2020 ◽  
Author(s):  
A. Moslemipour ◽  
S. Sadeghnejad ◽  
J. Gostick
Keyword(s):  
Image Processing ◽  
Signal To Noise Ratio ◽  
Ct Images ◽  
Micro Ct ◽  
Signal To Noise ◽  
Processing Approach ◽  
Noise Ratio

A New Method Using Chirp-Matching OPCPA Scheme for Improving Signal-to-Noise Ratio of Ultrashort Laser Pulse

2011 ◽  
Vol 31 (7) ◽  
pp. 0719001
Author(s):  
曾曙光 Zeng Shuguang ◽  
张彬 Zhang Bin ◽  
李现华 Li Xianhua ◽  
孙年春 Sun Nianchun ◽  
隋展 Sui Zhan
Keyword(s):  
Laser Pulse ◽  
Ultrashort Laser Pulse ◽  
Signal To Noise Ratio ◽  
New Method ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Ultrashort Laser
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