|Image Region Analyzer||Browse and filter connected components in an image|
|N-D filtering of multidimensional images|
|Create predefined 3-D filter|
|Filter region of interest (ROI) in image|
|General sliding-neighborhood operations|
|2-D Gaussian filtering of images|
|3-D Gaussian filtering of 3-D images|
|2-D median filtering|
|3-D median filtering|
|2-D order-statistic filtering|
|Local standard deviation of image|
|Local range of image|
|Local entropy of grayscale image|
|2-D box filtering of images|
|3-D box filtering of 3-D images|
|Enhance elongated or tubular structures in image|
|Maximum of Frobenius norm of Hessian of matrix|
|2-D convolution matrix|
|Bilateral filtering of images with Gaussian kernels|
|Estimate parameters for anisotropic diffusion filtering|
|Anisotropic diffusion filtering of images|
|Guided filtering of images|
|Non-local means filtering of image|
|Create high-resolution image from set of low-resolution burst mode images|
This example shows how to filter an image with a 5-by-5 averaging filter containing equal weights.
This example shows how to create a type of special filter called an unsharp masking filter, which makes edges and detail in an image appear sharper.
When a portion of the convolution or correlation kernel extends past the edge of an image, you can extrapolate image values by zero-padding the image or by replicating boundary pixels.
Guided image filtering performs edge-preserving smoothing on an image. It uses the content of a second image, called a guidance image, to influence the filtering.
This example shows how to reduce noise from an image while using a guidance image to preserve the sharpness of edges.
This example shows how to segment a hot object from the background in a thermographic image.
This example shows how to calculate the properties of regions in binary images by using the Image Region Analyzer app.
This example shows how to create a new binary image, such as a mask image, by filtering an existing binary image based on properties of regions in the image.
Integral images are a quick way to represent images for filtering. In an integral image, the value of each pixel is the summation of the pixels above and to the left of it.
This example shows how to smooth an image by different amounts by applying box filters of varying sizes to the integral image.
You can design filters that modify the frequency content of images. Filtering in the frequency domain is often faster than filtering in the spatial domain.