Image Sharpness Enhancement for Textile Testing

AATCC Review ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 43-53
Author(s):  
Jinfeng Zhou ◽  
Lingjie Yu ◽  
Rongwu Wang ◽  
Bugao Xu
Keyword(s):  
Image Sharpness ◽  
Sharpness Enhancement ◽  
Textile Testing

Image Sharpness Enhancement Using Adaptive 3X3 Convolution Masks

1985 ◽  
Vol 24 (4) ◽  
Author(s):  
Robin N. Strickland ◽  
Maged Y. Aly
Keyword(s):  
Image Sharpness ◽  
Sharpness Enhancement

scholarly journals An Image Sharpness Enhancement Algorithm Based on Green Function

2021 ◽  
Vol 38 (2) ◽  
pp. 513-519
Author(s):  
Qiuhe Huang
Keyword(s):  
Green Function ◽  
Original Image ◽  
Subjective Effect ◽  
Image Sharpness ◽  
The Poor ◽  
Gradient Domain ◽  
Sharpness Enhancement ◽  
The Green Function ◽  
Enhancement Algorithm ◽  
Traditional Image

The traditional image sharpness enhancement algorithm faces several defects, namely, the lack of details, and the poor subjective effect. To solve these defects, this paper proposes an image sharpness enhancement algorithm based on the Green function. Specifically, the Retinex model was employed to ensure that the enhanced image has outstanding details, and the Poisson’s equation was solved to maintain the consistency between the enhanced image and the original image in the gradient domain. Then, adaptive brightness mapping was carried out to determine the boundary conditions suitable for display, and the boundary of the region was sampled to reduce the complexity of our algorithm. Experimental results show that our algorithm improved the contrast and sharpness of images from the levels of contrastive image enhancement algorithms.

SEM image sharpness analysis

1996 ◽  
Vol 54 ◽  
pp. 142-143
Author(s):  
M. T. Postek ◽  
A. E. Vladar
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Quantitative Information ◽  
Primary Electron ◽  
Image Sharpness ◽  
Critical Dimensions ◽  
Sem Image ◽  
Frequency Changes ◽  
Electron Microscopes ◽  
Scanning Electron

Fully automated or semi-automated scanning electron microscopes (SEM) are now commonly used in semiconductor production and other forms of manufacturing. The industry requires that an automated instrument must be routinely capable of 5 nm resolution (or better) at 1.0 kV accelerating voltage for the measurement of nominal 0.25-0.35 micrometer semiconductor critical dimensions. Testing and proving that the instrument is performing at this level on a day-by-day basis is an industry need and concern which has been the object of a study at NIST and the fundamentals and results are discussed in this paper.In scanning electron microscopy, two of the most important instrument parameters are the size and shape of the primary electron beam and any image taken in a scanning electron microscope is the result of the sample and electron probe interaction. The low frequency changes in the video signal, collected from the sample, contains information about the larger features and the high frequency changes carry information of finer details. The sharper the image, the larger the number of high frequency components making up that image. Fast Fourier Transform (FFT) analysis of an SEM image can be employed to provide qualitiative and ultimately quantitative information regarding the SEM image quality.

scholarly journals Suppressing meta-holographic artifacts by laser coherence tuning

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Yaniv Eliezer ◽  
Geyang Qu ◽  
Wenhong Yang ◽  
Yujie Wang ◽  
Hasan Yılmaz ◽  
...  
Keyword(s):  
Spatial Coherence ◽  
Fine Tuning ◽  
Total Power ◽  
Image Sharpness ◽  
Continuous Tuning ◽  
Fine Spatial Resolution ◽  
Compact Size ◽  
Wide Range ◽  
Fabrication Defects ◽  
Holographic Displays

AbstractA metasurface hologram combines fine spatial resolution and large viewing angles with a planar form factor and compact size. However, it suffers coherent artifacts originating from electromagnetic cross-talk between closely packed meta-atoms and fabrication defects of nanoscale features. Here, we introduce an efficient method to suppress all artifacts by fine-tuning the spatial coherence of illumination. Our method is implemented with a degenerate cavity laser, which allows a precise and continuous tuning of the spatial coherence over a wide range, with little variation in the emission spectrum and total power. We find the optimal degree of spatial coherence to suppress the coherent artifacts of a meta-hologram while maintaining the image sharpness. This work paves the way to compact and dynamical holographic displays free of coherent defects.

scholarly journals Presenting a Semi-Automatic, Statistically-Based Approach to Assess the Sharpness Level of Optical Images from Natural Targets via the Edge Method. Case Study: The Landsat 8 OLI–L1T Data

2021 ◽  
Vol 13 (8) ◽  
pp. 1593
Author(s):  
Luca Cenci ◽  
Valerio Pampanoni ◽  
Giovanni Laneve ◽  
Carla Santella ◽  
Valentina Boccia
Keyword(s):  
Landsat 8 ◽  
Image Sharpness ◽  
Optical Images ◽  
Data Quality Assessment ◽  
Relevant Role ◽  
Edge Method ◽  
Selection Of ◽  
Statistical Criteria

Developing reliable methodologies of data quality assessment is of paramount importance for maximizing the exploitation of Earth observation (EO) products. Among the different factors influencing EO optical image quality, sharpness has a relevant role. When implementing on-orbit approaches of sharpness assessment, such as the edge method, a crucial step that strongly affects the final results is the selection of suitable edges to use for the analysis. Within this context, this paper aims at proposing a semi-automatic, statistically-based edge method (SaSbEM) that exploits edges extracted from natural targets easily and largely available on Earth: agricultural fields. For each image that is analyzed, SaSbEM detects numerous suitable edges (e.g., dozens-hundreds) characterized by specific geometrical and statistical criteria. This guarantees the repeatability and reliability of the analysis. Then, it implements a standard edge method to assess the sharpness level of each edge. Finally, it performs a statistical analysis of the results to have a robust characterization of the image sharpness level and its uncertainty. The method was validated by using Landsat 8 L1T products. Results proved that: SaSbEM is capable of performing a reliable and repeatable sharpness assessment; Landsat 8 L1T data are characterized by very good sharpness performance.

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