![]() The Gaussian Blur has a good level of image edge preservation, hence being used in edge detection operations.įrom Wikipedia we gain the following description:Ī Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function. When implementing image edge detection a Gaussian Blur is often applied to source/input images resulting in noise reduction. In contrast to the Box Blur method Gaussian Blurring produce resulting images appearing to contain a more uniform level of smoothing. ![]() The Gaussian method of image blurring is a popular and often implemented filter. The Mean Filter method can also be susceptible to directional artefacts. The Mean Filter Blur does not result in the same level of smoothing achieved by other image blur methods. The kernel consist of 25 elements, therefore the factor value equates to one divided by twenty five. The following is an example of a 5×5 Mean Filter convolution kernel: When performing image convolution implementing a Mean Filter kernel, the factor value equates to the 1 being divided by the sum of all kernel values. In most cases a Mean Filter matrix kernel will only contain the value one. Mean Filter as a title relates to all weight values in a convolution kernel being equal, therefore the alternate title of Box Blur. It is a form of low-pass ("blurring") filter and is a convolution.ĭue to its property of using equal weights it can be implemented using a much simpler accumulation algorithm which is significantly faster than using a sliding window algorithm. A Mean Filter definition can be found on Wikipedia as follows:Ī box blur is an image filter in which each pixel in the resulting image has a value equal to the average value of its neighbouring pixels in the input image. The Mean Filter also sometimes referred to as a Box Blur represents a fairly simplistic implementation and definition. In the following sections an overview of each method will be discussed. Each method provides a different set of desired properties and compromises. The image blurring technique capable of achieving optimal results will to varying degrees be dependent on the features present in the specified source/input image. Each of the supported methods in essence only represent a different convolution matrix kernel. In this article and the accompanying sample source code all methods of image blurring supported have been implemented through image convolution, with the exception of the Median filter. Image blurring can even be implemented in a fashion where results reflect image edge detection, a method known as Difference of Gaussians. In image edge detection implementations better results are often achieved when first implementing noise reduction through smoothing/ blurring. Often images are smoothed/blurred in order to remove/reduce image noise. Images perceived as too crisp/sharp can be softened by applying a variety of image blurring techniques and intensity levels. ![]() Images are often blurred as a method of smoothing an image. Image blurring results in image detail/ definition being perceived as less distinct. The process of image blurring can be regarded as reducing the sharpness or crispness defined by an image. The image below is a screenshot of the Image Blur Filter sample application in action: When a user selects an item from the combobox, the associated blur method will be implemented on the preview image. The combobox dropdown located on the right-hand side of the user interface lists all of the supported methods of image blurring. The sample application provides the user with the ability to select the method of image blurring to implement. In addition users are also able to save blurred result images when clicking the Save Image button and browsing the local file system. When clicking the Load Image button users are able to browse the local file system in order to select source/input images. ![]() The sample application is a Windows Forms based application of which the user interface enables the user to select an Image Blur type to implement. This article is accompanied by a sample application, intended to provide a means of testing and replicating topics discussed in this article. This article is accompanied by a sample source code Visual Studio project which is available for download here. The Image Blur methods covered in this article include: Box Blur, Gaussian Blur, Mean Filter, Median Filter and Motion Blur. This article serves to provides an introduction and discussion relating to Image Blurring methods and techniques.
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