Evaluation of De-Noising Algorithms for Amplitude Image Restoration in Digital Holography

Author(s):  
Silvio Montresor ◽  
Pascal Picart
2014 ◽  
Vol 22 (8) ◽  
pp. 2188-2195
Author(s):  
李琦 LI Qi ◽  
胡佳琦 HU Jia-qi ◽  
杨永发 YANG Yong-fa

Author(s):  
W.A. Carrington ◽  
F.S. Fay ◽  
K.E. Fogarty ◽  
L. Lifshitz

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.


Author(s):  
William H. Massover

Each molecule of ferritin (d = 130Å) contains a core of iron surrounded by a 24-subunit protein shell. The amount of iron stored is variable and is present within the central cavity (d = 80Å) as a hydrated ferric oxide equivalent to the mineral, ferrihydrite. Many early ultrastructural studies of ferritin detected regular patterns of a multiparticulate substructure in the iron-rich core [e.g., 3,4], Each small particle was termed a “micelle“; a theory became widely accepted that a core consisted of up to six micelles positioned at the vertices of an octahedron. Other workers recognized that the apparent micelles were smaller or even disappeared if images were recorded closer to exact focus [e.g., 5]. In 1969, Haydon clearly established that the observed substructure was really an imaging artifact; each apparent micelle was only a dot in the underfocused phase contrast image of the supporting film superimposed on the amplitude image of the strongly scattering metal.


Author(s):  
Richard B. Mott ◽  
John J. Friel ◽  
Charles G. Waldman

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.


1990 ◽  
Vol 137 (3) ◽  
pp. 163 ◽  
Author(s):  
V.A. Oliveira ◽  
J.M. Nightingale

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.


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