TU-CD-BRA-12: Coupling PET Image Restoration and Segmentation Using Variational Method with Multiple Regularizations

People are very familiar to the image. Image can be obtained by optical instrument observation of the objective world or image in the eyes of people of objective scenery in the human visual system. With the development of computer technology, digital image technology has been widely used in scientific research, industrial, health care, education, entertainment and communication. Therefore, the research of image technology has vital significance. This article mainly research image segmentation and image restoration based on variational method. Image segmentation is to divide the image into some meaningful area, is the foundation of subsequent target recognition and image understanding.

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Variational method for joint optical flow estimation and edge-aware image restoration

Pattern Recognition ◽

10.1016/j.patcog.2016.10.027 ◽

2017 ◽

Vol 65 ◽

pp. 11-25 ◽

Cited By ~ 14

Author(s):

Zhigang Tu ◽

Wei Xie ◽

Jun Cao ◽

Coert van Gemeren ◽

Ronald Poppe ◽

...

Keyword(s):

Variational Method ◽

Optical Flow ◽

Image Restoration ◽

Flow Estimation ◽

Optical Flow Estimation

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An efficient nonlocal variational method with application to underwater image restoration

Neurocomputing ◽

10.1016/j.neucom.2019.08.041 ◽

2019 ◽

Vol 369 ◽

pp. 106-121 ◽

Cited By ~ 11

Author(s):

Guojia Hou ◽

Zhenkuan Pan ◽

Guodong Wang ◽

Huan Yang ◽

Jinming Duan

Keyword(s):

Variational Method ◽

Image Restoration ◽

Underwater Image

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Bayesian Image Restoration for Poisson Corrupted Image Using a Latent Variational Method with Gaussian MRF

IPSJ Online Transactions ◽

10.2197/ipsjtrans.8.15 ◽

2015 ◽

Vol 8 (0) ◽

pp. 15-24 ◽

Cited By ~ 1

Author(s):

Hayaru Shouno

Keyword(s):

Variational Method ◽

Image Restoration

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

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100102286 ◽

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.

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Improving x-ray map resolution with image restoration techniques

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100165458 ◽

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.

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