Optimum adaptive array stochastic resonance in noisy grayscale image restoration

2019 ◽  
Vol 383 (13) ◽  
pp. 1457-1465 ◽  
Author(s):  
Jian Liu ◽  
Bing Hu ◽  
Youguo Wang
Keyword(s):  
Image Restoration ◽  
Stochastic Resonance ◽  
Adaptive Array ◽  
Grayscale Image

scholarly journals Image Restoration Based on Stochastic Resonance in a Parallel Array of Fitzhugh–Nagumo Neuron

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Huage Zhang ◽  
Jinfei Yu ◽  
Yumei Ma ◽  
Zhenkuan Pan ◽  
Jingjing Zhao
Keyword(s):  
Image Restoration ◽  
Stochastic Resonance ◽  
Pulse Amplitude ◽  
Grayscale Image ◽  
Noise Condition ◽  
Scanning Method ◽  
Parallel Array ◽  
Image Details ◽  
Restoration Effect ◽  
Grayscale Images

The poor denoising effect for noisy grayscale images with traditional processing methods would be obtained under strong noise condition, and some image details would be lost. In this paper, a parallel array model of Fitzhugh–Nagumo (FHN) neurons was proposed, which can restore noisy grayscale images well with low peak signal-to-noise ratio (PSNR) conditions and the image details are better preserved. Firstly, the row-column scanning method was used to convert the 2D grayscale image into a 1D signal, and then the 1D signal was converted into a binary pulse amplitude modulation (BPAM) signal by signal modulation. The modulated signal was input to an FHN parallel array for stochastic resonance (SR). Finally, the array output signal was restored to a 2D gray image, and the image restoration effect was analyzed based on the PSNR and Structural SIMilarity (SSIM) index. It is shown that the SR effect can be exhibited in an array of FHN neuron nonlinearities by increasing the array size, and the image restoration effect is significantly better than the traditional image restoration method, and larger PSNR and SSIM can be obtained. It provides a new idea for grayscale image restoration in a low PSNR environment.

Multi-Channel and Multi-Model-Based Autoencoding Prior for Grayscale Image Restoration

2020 ◽  
Vol 29 ◽  
pp. 142-156 ◽  
Author(s):  
Sanqian Li ◽  
Binjie Qin ◽  
Jing Xiao ◽  
Qiegen Liu ◽  
Yuhao Wang ◽  
...  
Keyword(s):  
Image Restoration ◽  
Grayscale Image ◽  
Model Based

scholarly journals Multi-Noise and Multi-Channel Derived Prior Information for Grayscale Image Restoration

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 150082-150092 ◽  
Author(s):  
Minghui Zhang ◽  
Yuan Yuan ◽  
Fengqin Zhang ◽  
Siyuan Wang ◽  
Shanshan Wang ◽  
...  
Keyword(s):  
Image Restoration ◽  
Prior Information ◽  
Grayscale Image

Analysis of 3-d molecular distribution with the digital imaging microscope

1988 ◽  
Vol 46 ◽  
pp. 40-41
Author(s):  
W.A. Carrington ◽  
F.S. Fay ◽  
K.E. Fogarty ◽  
L. Lifshitz
Keyword(s):  
Image Restoration ◽  
Digital Imaging ◽  
Fluorescent Dyes ◽  
Optical Axis ◽  
Computer Hardware ◽  
Computer Algorithms ◽  
Low Contrast ◽  
Specific Proteins ◽  
Effective Use ◽  
Single Living Cells

Advances in digital imaging microscopy and in the synthesis of fluorescent dyes allow the determination of 3D distribution of specific proteins, ions, GNA or DNA in single living cells. Effective use of this technology requires a combination of optical and computer hardware and software for image restoration, feature extraction and computer graphics.The digital imaging microscope consists of a conventional epifluorescence microscope with computer controlled focus, excitation and emission wavelength and duration of excitation. Images are recorded with a cooled (-80°C) CCD. 3D images are obtained as a series of optical sections at .25 - .5 μm intervals.A conventional microscope has substantial blurring along its optical axis. Out of focus contributions to a single optical section cause low contrast and flare; details are poorly resolved along the optical axis. We have developed new computer algorithms for reversing these distortions. These image restoration techniques and scanning confocal microscopes yield significantly better images; the results from the two are comparable.

Improving x-ray map resolution with image restoration techniques

1996 ◽  
Vol 54 ◽  
pp. 598-599
Author(s):  
Richard B. Mott ◽  
John J. Friel ◽  
Charles G. Waldman
Keyword(s):  
Image Restoration ◽  
Hubble Space Telescope ◽  
Extensive Literature ◽  
Small Object ◽  
X Rays ◽  
X Ray ◽  
Mapping Parameters ◽  
The Matrix ◽  
The 1960S ◽  
Map Resolution

X-rays are emitted from a relatively large volume in bulk samples, limiting the smallest features which are visible in X-ray maps. Beam spreading also hampers attempts to make geometric measurements of features based on their boundaries in X-ray maps. This has prompted recent interest in using low voltages, and consequently mapping L or M lines, in order to minimize the blurring of the maps.An alternative strategy draws on the extensive work in image restoration (deblurring) developed in space science and astronomy since the 1960s. A recent example is the restoration of images from the Hubble Space Telescope prior to its new optics. Extensive literature exists on the theory of image restoration. The simplest case and its correspondence with X-ray mapping parameters is shown in Figures 1 and 2.Using pixels much smaller than the X-ray volume, a small object of differing composition from the matrix generates a broad, low response. This shape corresponds to the point spread function (PSF). The observed X-ray map can be modeled as an “ideal” map, with an X-ray volume of zero, convolved with the PSF. Figure 2a shows the 1-dimensional case of a line profile across a thin layer. Figure 2b shows an idealized noise-free profile which is then convolved with the PSF to give the blurred profile of Figure 2c.

Maximum image restoration in nuclear medicine

1990 ◽  
Vol 137 (3) ◽  
pp. 163 ◽  
Author(s):  
V.A. Oliveira ◽  
J.M. Nightingale
Keyword(s):  
Nuclear Medicine ◽  
Image Restoration

Stochastic resonance in the peripheral auditory system

2014 ◽  
Vol 2 ◽  
pp. 417-420
Author(s):  
Florian Gomez ◽  
Stefan Martignoli ◽  
Ruedi Stoop
Keyword(s):  
Stochastic Resonance ◽  
Auditory System ◽  
Peripheral Auditory System
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