What does FGR mean in UNCLASSIFIED
FGR (Filter Grid Ratio) is a measurement used in image processing to quantify the relative prominence of high-frequency components in an image. It is calculated by dividing the sum of the absolute values of the high-frequency coefficients of a Fourier transform of the image by the sum of the absolute values of all coefficients.
FGR meaning in Unclassified in Miscellaneous
FGR mostly used in an acronym Unclassified in Category Miscellaneous that means Filter Grid Ratio
Shorthand: FGR,
Full Form: Filter Grid Ratio
For more information of "Filter Grid Ratio", see the section below.
How FGR Works
- The image is converted to the frequency domain using a Fourier transform.
- The resulting frequency spectrum is divided into two parts: high-frequency and low-frequency coefficients.
- The FGR is calculated by taking the ratio of the sum of the absolute values of the high-frequency coefficients to the sum of the absolute values of all coefficients.
Significance of FGR
- A high FGR indicates that the image contains a significant amount of high-frequency detail, such as sharp edges and fine textures.
- A low FGR indicates that the image is dominated by low-frequency components, such as smooth areas and gradual transitions.
- FGR is useful for image enhancement, noise reduction, and feature extraction.
Applications of FGR
- Image Sharpening: Increasing the FGR can enhance the sharpness of an image by boosting high-frequency components.
- Noise Reduction: Decreasing the FGR can reduce noise by suppressing high-frequency components.
- Feature Extraction: FGR can be used to identify and extract specific features from an image, such as edges and corners.
Essential Questions and Answers on Filter Grid Ratio in "MISCELLANEOUS»UNFILED"
What is Filter Grid Ratio (FGR)?
Filter Grid Ratio (FGR) is a parameter used in X-ray imaging to assess the balance between image quality and radiation dose. It is the ratio of the thickness of the grid to the interspace between the grid lines.
Final Words: FGR is a valuable metric in image processing that provides insights into the distribution of frequency components in an image. By understanding the concept and applications of FGR, image analysts and developers can effectively enhance, denoise, and extract features from images.
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