|Image Region Analyzer||Browse and filter connected components in an image|
|N-D filtering of multidimensional images|
|Create predefined 2-D filter|
|2-D Gaussian filtering of images|
|3-D Gaussian filtering of 3-D images|
|Guided filtering of images|
|Normalized 2-D cross-correlation|
|2-D adaptive noise-removal filtering|
|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|
|General sliding-neighborhood operations|
|2-D box filtering of images|
|3-D box filtering of 3-D images|
This example shows how to filter an image with a 5-by-5 averaging filter containing equal weights.
This example shows how to filter the three color channels of an RGB image.
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.
This example shows how to filter a binary image based on properties, such as area and perimeter, of objects in the image.
This example shows how to filter an image using convolution, and compares the result to filtering using correlation.
Noise refers to random error in pixel values acquired during image acquisition or transmission. Removing noise can improve image quality.
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 blur an image using Gaussian smoothing filters of different strengths. The example includes isotropic and anisotroptic Gaussian filtering.
This example shows how to segment a hot object from the background in a thermographic image.
This example shows how to smooth an image by different amounts by applying box filters of varying sizes to the integral image.
This example shows how to reduce noise associated with computing image gradients.
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.
In a spatially filtered image, the value of each output pixel is the weighted sum of neighboring input pixels. The weights are provided by a matrix called the convolution kernal or filter.
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.
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.
The toolbox filters images using two-dimensional finite impulse response (FIR) filters, which are represented as matrices. FIR filters are easy to design and implement, and are more stable than other filter classes.