Coherence-Enhancing Filtering implements an automatic image and video abstraction technique based on adaptive line integral convolution and directional shock filtering.
You can load your image file and adjust the default settings of the filters t match your liking and then save the result.

 

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Coherence-Enhancing Filtering 2022 Crack can reduce and enhance the frequency coherence of natural and man-made images. The image can be segmented by a procedural boundary detection algorithm. A pseudo-blur filter is used to demarcate different types of contours. Peripheral regions can be enhanced or suppressed based on the detected contours.
You can use the image in different ways and achieve different visual effects.
The function of the image can be adjusted in a number of different ways, the user can choose to apply the image to a gray-scale image, RGB image, or HSV image.
You can choose between a traditional filter mode and an adaptive mode. The adaptive mode uses a non-directional adaptive line integral convolution technique (ALIC) to segment the image into different regions based on the contrast and illumination. The system applies a directional shock filter to segment the image into figure and background regions. You can balance between the filter parameters to get the best effect for any given image.

Here’s a demonstration of the Coherence-Enhancing Filtering:

You can download the software to try it for yourself by following the links below:

Source

2nd Source

3rd Source

Video Demo

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Coherence-Enhancing Filtering With License Code

This is a zoom-in of a droplet on a glass slide, showing in greater detail how the ImageJ high-pass transition filter enhances the contrast of the object and preserves the details.

Line-Integral Convolution (LIC) is used to increase the contrast by detecting the steep transitions between the high- and low-intensity regions of the image.
Adaptive Shock Filtering (ASF) is used to reduce the high-frequency noise by making the contrast smoother.
The Line-Integral-Convolution detector is a two-step approach.
First, threshold detection is implemented using a peak-seeking technique and an adaptive peak threshold is then applied to enhance the contrast in high-intensity regions.

The ASF method uses the Lanczos technique to smooth out sharp edges and reduce the high-frequency noise to give a pleasing look to the picture.

Implementation:

To change the default settings of the filters, we must go to ImageJ > Adjustments… > High Pass > Line Integral Convolution > Coherence-Enhancing Filtering > Apply
Once loaded, adjust your settings accordingly and save the result
To create a picture of high contrast, click Create > Apply.
To create a picture with better, smooth gradients, click Create > Apply > Adaptive Shock Filtering > Apply

Dimensions:

(exact) (relative) to display (keeping original aspect ratio):

The result image will be opened in a new window.
The original image is shrunken to enable a better view
The original image is zoomed in, so that the full width of the original image is visible on the screen.
The original image keeps its original aspect ratio and it is enlarged to fit the screen.

Return to High Pass Filter.
Change the settings as shown below.

Original height: x 2.1 x original width:

threshold of the line integral convolution : red curve.
contrast threshold: green line.
Adaptive threshold.
contrast threshold (magnitude difference): red line.
contrast threshold (magnitude difference): blue line.

Once the settings are set, click on Apply.
If the picture is too blurred, we must increase the setting Contrast Threshold (magnitude difference).

We now use the same setting to create a second picture of high contrast, but with better gradients.
For this, click on Adaptive
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For educational purposes only.
You can download and test the application here:

This is an application for repairing images that are blurred due to defocusing and motion blur.
Note: The effect is based on a new method for producing sharp images with blur. The resulting images display a crisper image, which is sharpened with the diffusion effect.
Those images will be more coherent and therefore more natural, than before.
This is an automatic method of solving the blurry images that are caused when they are sharpened by hand.
This new method has been tested in a significant amount of images that have been sharpened by hand. The results show that the CEF method does indeed perform better.
Automatic Sharpen Images is much faster and more precise than other sharpeners available on the market. The results provided by the program are a sharp image, not sharpened with a gradient filter.
In the case of a relatively weak signal (for example, in low light), the image is still a bit too blurry and appears too noisy.
Automatic Sharpen Images uses adaptive filters to analyze the signal, calculate the edges and find the optimal level of sharpening.
In the case of a stronger signal, the program can detect the sharpening effects more accurately and make the images sharper.
The application will automatically decide which edge is the most important for sharpening.
The noise of a signal is not automatically removed, because in many cases, those images are more interesting, because of their textures and colors, than the perfect image produced by the method.
The application will adaptively choose the optimal filtering parameters, depending on the strength of the signal and its content.
Requirements:
The application is based on the following software:
-Java 5
-Java 6 or higher
-Java 7 will be supported in a future update
Download
-JRE
-jython (zip archive)
-Nexus 5 (zip archive)
-GCC 4.9.x on Windows
Installation
-The user is required to install the application with Java
-The application runs with a memory size of more than 1 GB. However, we recommend to install at least 2 GB
-When installed in the operating system, install the application using the same path that you use to install the operating system
-To extract the archive, you

What’s New In?

Select the landscape image or video
Adjust the default Coherence-Enhancing filtering parameters:

Center Fuzziness: Set the amount of blur
Resolution Fuzziness: Set the degree of the fuzzy edge in the image
Lower Coherence: Set the lower cut of the Fourier spectrum,
Linear Gradient: Set the Gradient of the Linear kernel
Angular Gradient: Set the Gradient of the Angular kernel
Color Imbalance: Set the amount of imbalance in the color tone
Coherence: Set the degree of the low spatial coherence

The result of an image processed by Coherence-Enhancing Filtering is visible in the gallery below:

Assign Folder for saving the image(s):

Note: Coherence-Enhancing Filtering can be applied to images in the Image tab of the toolbox.

What’s new
You can test the new Coherence-Enhancing Filtering using the blinkenlights test file.

References

External links
Coherence-Enhancing Filtering Tutorial
Coherence-Enhancing Filtering at GraphicRiver
Coherence-Enhancing Filtering on the website of UWC…
Coherence-Enhancing Filtering at the website of Xiaoice Vision

Category:Digital photography
Category:Image processingAbout the Author

David Cameron is a writer and editor, broadcaster, campaigner and politician.

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David Cameron is a writer and editor, broadcaster, campaigner and politician.

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System Requirements For Coherence-Enhancing Filtering:

Windows 10 Anniversary Update (Creators Update) and newer OS, or macOS version 10.12 Sierra or newer, or Linux version 4.3 or newer.
Minimum hardware requirements:
Windows 10 64-bit operating system with 8 GB of RAM.
5,554 MB of disk space available, free on all hard drive partitions.
An Intel Core 2 Duo processor with 2 GB of RAM, or AMD Athlon X2 Dual Core or later with 2 GB of RAM, or Intel Pentium Dual Core 3.0 GHz or faster

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