Fast and high quality learning-based super-resolution utilizing TV regularization method

2011 ◽  
Author(s):  
Tomio Goto ◽  
Shotaro Suzuki ◽  
Satoshi Hirano ◽  
Masaru Sakurai ◽  
Truong Q. Nguyen
Keyword(s):  
Regularization Method ◽  
Super Resolution ◽  
High Quality ◽  
Tv Regularization ◽  
Quality Learning

Designing and Developing Competency-Based Education Courses Using Standards

2017 ◽  
pp. 232-249 ◽  
Author(s):  
Nancy B. Hastings ◽  
Karen L. Rasmussen
Keyword(s):  
Learning Environments ◽  
High Quality ◽  
Competency Based Education ◽  
Policies And Procedures ◽  
Quality Product ◽  
Competency Based ◽  
Micro Level ◽  
Quality Learning ◽  
Education Courses ◽  
High Quality Product

Standards provide designers and developers of competency-based education courses and programs with a structure and framework that serve as a way to create quality learning environments that align objectives, instruction, and assessments. At the micro-level, standards facilitate direction of the structure, format, and content of a competency-based course that ensures a high-quality product. At the macro-level, standards help institutional administrators and faculty make good, informed decisions about program policies and procedures.

Learning4Content

2015 ◽  
pp. 1903-1914
Author(s):  
Ramesh C. Sharma
Keyword(s):  
Common Factors ◽  
Open Educational Resources ◽  
Design Development ◽  
Educational Materials ◽  
High Quality ◽  
Mass Collaboration ◽  
Open Networks ◽  
The World ◽  
Quality Learning

The world over, some common factors have contributed to the emergence and growth of open educational resources. These can be to increase access to educational materials, to reduce the costs, to enhance the quality of educational content through working collaboratively, and to be used for capacity building and research. The WikiEducator project has been the foremost initiative to turn digital divide into digital dividends through free content and open networks. WikiEducator was established on 1 May 2006, and since then, it has grown a very big network of more than 66,700 registered WikiEducators. Learning4Content is one of the flagship initiative of WikiEducator providing free training for teachers. In this chapter, the author discusses building a vibrant and sustainable global community contributing to design, development, and delivery of free content for learning and providing training to develop wiki skills for mass collaboration to create high quality learning resources.

scholarly journals Artifact-free whole-slide imaging with structured illumination microscopy and Bayesian image reconstruction

GigaScience ◽  
2020 ◽  
Vol 9 (4) ◽  
Author(s):  
Karl A Johnson ◽  
Guy M Hagen
Keyword(s):  
Super Resolution ◽  
Structured Illumination ◽  
Optical Sectioning ◽  
Structured Illumination Microscopy ◽  
High Quality ◽  
Related Data ◽  
Reconstruction Methods ◽  
Set Up ◽  
Bayesian Image Reconstruction ◽  
Hematoxylin Eosin

Abstract Background Structured illumination microscopy (SIM) is a method that can be used to image biological samples and can achieve both optical sectioning and super-resolution effects. Optimization of the imaging set-up and data-processing methods results in high-quality images without artifacts due to mosaicking or due to the use of SIM methods. Reconstruction methods based on Bayesian estimation can be used to produce images with a resolution beyond that dictated by the optical system. Findings Five complete datasets are presented including large panoramic SIM images of human tissues in pathophysiological conditions. Cancers of the prostate, skin, ovary, and breast, as well as tuberculosis of the lung, were imaged using SIM. The samples are available commercially and are standard histological preparations stained with hematoxylin-eosin. Conclusion The use of fluorescence microscopy is increasing in histopathology. There is a need for methods that reduce artifacts caused by the use of image-stitching methods or optical sectioning methods such as SIM. Stitched SIM images produce results that may be useful for intraoperative histology. Releasing high-quality, full-slide images and related data will aid researchers in furthering the field of fluorescent histopathology.

scholarly journals Depth image super-resolution reconstruction based on a modified joint trilateral filter

2019 ◽  
Vol 6 (1) ◽  
pp. 181074 ◽  
Author(s):  
Dongsheng Zhou ◽  
Ruyi Wang ◽  
Xin Yang ◽  
Qiang Zhang ◽  
Xiaopeng Wei
Keyword(s):  
Super Resolution ◽  
Structural Similarity ◽  
Depth Image ◽  
High Quality ◽  
Depth Images ◽  
Trilateral Filter ◽  
Coding Method ◽  
Image Edge ◽  
Image Super Resolution ◽  
Sr Reconstruction

Depth image super-resolution (SR) is a technique that uses signal processing technology to enhance the resolution of a low-resolution (LR) depth image. Generally, external database or high-resolution (HR) images are needed to acquire prior information for SR reconstruction. To overcome the limitations, a depth image SR method without reference to any external images is proposed. In this paper, a high-quality edge map is first constructed using a sparse coding method, which uses a dictionary learned from the original images at different scales. Then, the high-quality edge map is used to guide the interpolation for depth images by a modified joint trilateral filter. During the interpolation, some information of gradient and structural similarity (SSIM) are added to preserve the detailed information and suppress the noise. The proposed method can not only preserve the sharpness of image edge, but also avoid the dependence on database. Experimental results show that the proposed method is superior to some state-of-the-art depth image SR methods.

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