Submission declined on 8 May 2024 by
DoubleGrazing (
talk). This submission is not adequately supported by
reliable sources. Reliable sources are required so that information can be
verified. If you need help with referencing, please see
Referencing for beginners and
Citing sources. This draft's references do not show that the subject
qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are:
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
| ![]() |
The High Boost Filter is a non-linear digital filtering technique. The High Boost Filter, also known as High Boost Filtering, is a fundamental concept in the field of digital image processing. It is a technique utilized to enhance the sharpness and details of an image by accentuating its high-frequency components while preserving its low-frequency components. [1]
The fundamental concept behind the High Boost Filter involves selectively amplifying high-frequency components within an image while preserving its low-frequency details. Analogous to traditional linear filters, the High Boost Filter operates on a non-linear paradigm, accentuating image sharpness by enhancing edges and fine details. This process begins with the extraction of high-frequency components through high-pass filtering, followed by amplification and integration with the original image. Unlike traditional filters that rely on weighted averages, the High Boost Filter's emphasis on frequency manipulation enables precise enhancement without sacrificing edge integrity. The filter's versatility extends to various dimensions, accommodating both one-dimensional and multidimensional data, ensuring comprehensive detail enhancement across diverse image types.
HPF = Original image - Low frequency components LPF = Original image - High frequency components HBF = A * Original image - Low frequency components = (A - 1) * Original image + [Original image - Low frequency components] = (A - 1) * Original image + HPF
Here,
% MatLab code for High Boost Filtering
% read the image in variable 'a'
a=imread("your_image.jpg");
% Define the High Boost Filter
% with central value=4 and A=1.
HBF=[0 -1 0; -1 5 -1; 0 -1 0];
% Convolve the image 'a' with HBF.
a1=convn(a, HBF, 'same');
% Normalise the intensity values.
a2=uint8(a1);
%Display the sharpened image.
imtool(a2,[]);
% Define the HBF with Central value=8 and A=1.
SHBF=[-1 -1 -1; -1 9 -1; -1 -1 -1];
% Convolve the image 'a' with HBF.
a3=convn(a,SHBF, 'same');
% Normalise the intensity values.
a4=uint8(a3);
% Display the sharpened image.
imtool(a4,[]);
Given an original grayscale image with pixel values ranging from to , apply a High Boost Filter with an amplification factor and a kernel size of . The low-pass filtering operation is performed using a simple averaging filter. If a specific pixel in the original image has a value of and its neighboring pixels within the kernel have the following values:, compute the corresponding pixel value in the resulting High Boost Filtered image.
4. Compute the resulting pixel value in the High Boost Filtered image:
Submission declined on 8 May 2024 by
DoubleGrazing (
talk). This submission is not adequately supported by
reliable sources. Reliable sources are required so that information can be
verified. If you need help with referencing, please see
Referencing for beginners and
Citing sources. This draft's references do not show that the subject
qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are:
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
| ![]() |
The High Boost Filter is a non-linear digital filtering technique. The High Boost Filter, also known as High Boost Filtering, is a fundamental concept in the field of digital image processing. It is a technique utilized to enhance the sharpness and details of an image by accentuating its high-frequency components while preserving its low-frequency components. [1]
The fundamental concept behind the High Boost Filter involves selectively amplifying high-frequency components within an image while preserving its low-frequency details. Analogous to traditional linear filters, the High Boost Filter operates on a non-linear paradigm, accentuating image sharpness by enhancing edges and fine details. This process begins with the extraction of high-frequency components through high-pass filtering, followed by amplification and integration with the original image. Unlike traditional filters that rely on weighted averages, the High Boost Filter's emphasis on frequency manipulation enables precise enhancement without sacrificing edge integrity. The filter's versatility extends to various dimensions, accommodating both one-dimensional and multidimensional data, ensuring comprehensive detail enhancement across diverse image types.
HPF = Original image - Low frequency components LPF = Original image - High frequency components HBF = A * Original image - Low frequency components = (A - 1) * Original image + [Original image - Low frequency components] = (A - 1) * Original image + HPF
Here,
% MatLab code for High Boost Filtering
% read the image in variable 'a'
a=imread("your_image.jpg");
% Define the High Boost Filter
% with central value=4 and A=1.
HBF=[0 -1 0; -1 5 -1; 0 -1 0];
% Convolve the image 'a' with HBF.
a1=convn(a, HBF, 'same');
% Normalise the intensity values.
a2=uint8(a1);
%Display the sharpened image.
imtool(a2,[]);
% Define the HBF with Central value=8 and A=1.
SHBF=[-1 -1 -1; -1 9 -1; -1 -1 -1];
% Convolve the image 'a' with HBF.
a3=convn(a,SHBF, 'same');
% Normalise the intensity values.
a4=uint8(a3);
% Display the sharpened image.
imtool(a4,[]);
Given an original grayscale image with pixel values ranging from to , apply a High Boost Filter with an amplification factor and a kernel size of . The low-pass filtering operation is performed using a simple averaging filter. If a specific pixel in the original image has a value of and its neighboring pixels within the kernel have the following values:, compute the corresponding pixel value in the resulting High Boost Filtered image.
4. Compute the resulting pixel value in the High Boost Filtered image: