Is a Single Image Sufficient for Evolving Edge Features by Genetic Programming?

2014 ◽  
pp. 451-463 ◽  
Author(s):  
Wenlong Fu ◽  
Mark Johnston ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Single Image ◽  
Edge Features

scholarly journals Image Super-Resolution Based on Sparse Representation via Direction and Edge Dictionaries

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xuan Zhu ◽  
Xianxian Wang ◽  
Jun Wang ◽  
Peng Jin ◽  
Li Liu ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Super Resolution ◽  
Training Image ◽  
Image Features ◽  
Single Image ◽  
Edge Structure ◽  
Image Patches ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Edge Features

Sparse representation has recently attracted enormous interests in the field of image super-resolution. The sparsity-based methods usually train a pair of global dictionaries. However, only a pair of global dictionaries cannot best sparsely represent different kinds of image patches, as it neglects two most important image features: edge and direction. In this paper, we propose to train two novel pairs of Direction and Edge dictionaries for super-resolution. For single-image super-resolution, the training image patches are, respectively, divided into two clusters by two new templates representing direction and edge features. For each cluster, a pair of Direction and Edge dictionaries is learned. Sparse coding is combined with the Direction and Edge dictionaries to realize super-resolution. The above single-image super-resolution can restore the faithful high-frequency details, and the POCS is convenient for incorporating any kind of constraints or priors. Therefore, we combine the two methods to realize multiframe super-resolution. Extensive experiments on image super-resolution are carried out to validate the generality, effectiveness, and robustness of the proposed method. Experimental results demonstrate that our method can recover better edge structure and details.

Indoor scene modeling from a single image using normal inference and edge features

2017 ◽  
Vol 33 (10) ◽  
pp. 1227-1240 ◽  
Author(s):  
Mingming Liu ◽  
Yanwen Guo ◽  
Jun Wang
Keyword(s):  
Scene Modeling ◽  
Single Image ◽  
Indoor Scene ◽  
Edge Features

Probing the Chemistry and Spectroscopy of Radiation-Sensitive Polymers by Parallel-Detection EELS

1990 ◽  
Vol 48 (2) ◽  
pp. 34-35
Author(s):  
E. G. Rightor ◽  
G. P. Young
Keyword(s):  
Electron Beam ◽  
Bisphenol A ◽  
Energy Loss ◽  
Parallel Detection ◽  
Commercial Grade ◽  
Incident Electron Beam ◽  
Ptfe Sample ◽  
Spectroscopic Information ◽  
Edge Features ◽  
Electron Energy Loss

Investigation of neat polymers by TEM is often thwarted by their sensitivity to the incident electron beam, which also limits the usefulness of chemical and spectroscopic information available by electron energy loss spectroscopy (EELS) for these materials. However, parallel-detection EELS systems allow reduced radiation damage, due to their far greater efficiency, thereby promoting their use to obtain this information for polymers. This is evident in qualitative identification of beam sensitive components in polymer blends and detailed investigations of near-edge features of homopolymers.Spectra were obtained for a poly(bisphenol-A carbonate) (BPAC) blend containing poly(tetrafluoroethylene) (PTFE) using a parallel-EELS and a serial-EELS (Gatan 666, 607) for comparison. A series of homopolymers was also examined using parallel-EELS on a JEOL 2000FX TEM employing a LaB6 filament at 100 kV. Pure homopolymers were obtained from Scientific Polymer Products. The PTFE sample was commercial grade. Polymers were microtomed on a Reichert-Jung Ultracut E and placed on holey carbon grids.

Sonography of subcutaneous tissue cannot determine causes of lower limb edema

VASA ◽  
2015 ◽  
Vol 44 (2) ◽  
pp. 122-128 ◽  
Author(s):  
Mandy Becker ◽  
Tom Schilling ◽  
Olga von Beckerath ◽  
Knut Kröger
Keyword(s):  
Clinical Diagnosis ◽  
Lower Limb ◽  
Subcutaneous Tissue ◽  
Hepatic Disease ◽  
Clinical Use ◽  
Single Image ◽  
Heart Insufficiency ◽  
Chronic Hepatic Disease ◽  
Limb Edema ◽  
Venous Disorders

Background: To clarify the clinical use of sonography for differentiation of edema we tried to answer the question whether a group of doctors can differentiate lymphedema from cardiac, hepatic or venous edema just by analysing sonographic images of the edema. Patients and methods: 38 (70 ± 12 years, 22 (58 %) females) patients with lower limb edema were recruited according the clinical diagnosis: 10 (26 %) lymphedema, 16 (42 %) heart insufficiency, 6 (16 %) venous disorders, 6 (16 %) chronic hepatic disease. Edema was depicted sonographically at the most affected leg in a standardised way at distal and proximal calf. 38 sets of images were anonymised and send to 5 experienced doctors. They were asked whether they can see criteria for lymphedema: 1. anechoic gaps, 2. horizontal gaps and 3. echoic rims. Results: Accepting an edema as lymphedema if only one doctor sees at least one of the three criteria for lymphatic edema on each single image all edema would be classified as lymphatic. Accepting lymphedema only if all doctors see at least one of the three criteria on the distal image of the same patient 80 % of the patients supposed to have lymphedema are classified as such, but also the majority of cardiac, venous and hepatic edema. Accepting lymphedema only if all doctors see all three criteria on the distal image of the same patients no edema would be classified as lymphatic. In addition we separated patients by Stemmers’ sign in those with positive and negative sign. The interpretation of the images was not different between both groups. Conclusions: Our analysis shows that it is not possible to differentiate lymphedema from other lower limb edema sonographically.

Single Image Dehazing by Predicting Atmospheric Scattering Parameters

2020 ◽  
Vol 2020 (1) ◽  
pp. 74-77
Author(s):  
Simone Bianco ◽  
Luigi Celona ◽  
Flavio Piccoli
Keyword(s):  
Reference Image ◽  
Single Image ◽  
Scattering Parameters ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Local Structures ◽  
Full Reference ◽  
Reconstruction Quality ◽  
Single Image Dehazing

In this work we propose a method for single image dehazing that exploits a physical model to recover the haze-free image by estimating the atmospheric scattering parameters. Cycle consistency is used to further improve the reconstruction quality of local structures and objects in the scene as well. Experimental results on four real and synthetic hazy image datasets show the effectiveness of the proposed method in terms of two commonly used full-reference image quality metrics.

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