Filtering is a technique for modifying or enhancing an image. For example, you can filter an image to emphasize certain features or remove other features. Image processing operations implemented with filtering include smoothing, sharpening, and edge enhancement.
|Image Region Analyzer
|Browse and filter connected components in an image
Basic Image Filtering
|N-D filtering of multidimensional images
|Filter region of interest (ROI) in grayscale image
|General sliding-neighborhood operations
|2-D Gaussian filtering of images
|3-D Gaussian filtering of 3-D images
|2-D adaptive noise-removal filtering
|2-D median filtering
|3-D median filtering
|2-D and 3-D mode filtering (Since R2020a)
|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 using Frangi vesselness filter
|Maximum of Frobenius norm of Hessian of 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 (Since R2019a)
Filtering By Property Characteristics
Integral Image Domain Filtering
Design Image Filters
|Create predefined 2-D filter
|Create predefined 3-D filter
|2-D convolution matrix
|2-D frequency response
|2-D FIR filter using frequency sampling
|2-D FIR filter using frequency transformation
|2-D FIR filter using 1-D window method
|2-D FIR filter using 2-D window method
|Frequency spacing for frequency response
Getting Started with Image Filtering in the Spatial Domain
- What Is Image 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 kernel or filter.
- Filter Grayscale and Truecolor (RGB) Images Using imfilter Function
Filter an image with a 5-by-5 averaging filter containing equal weights.
- Filter Images Using Predefined Filter
Create a type of special filter called an unsharp masking filter, which makes edges and detail in an image appear sharper.
- imfilter Boundary Padding Options
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.
- Noise Removal
Remove image noise by using techniques such as averaging filtering, median filtering, and adaptive filtering based on local image variance.
- Apply Gaussian Smoothing Filters to Images
Reduce image noise by blurring the image using isotropic and anisotropic Gaussian smoothing filters of different strengths.
- Reduce Noise in Image Gradients
Reduce noise associated with computing image gradients so that features can be more accurately detected.
- What is Guided Image Filtering?
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.
- Perform Flash/No-flash Denoising with Guided Filter
Reduce noise in an image while using a guidance image to preserve the sharpness of edges.
- Segment Thermographic Image After Edge-Preserving Filtering
Segment a hot object from the background in a thermographic image.
Integral Image Domain Filtering
- Integral 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.
- Apply Multiple Filters to Integral Image
Smooth an image by different amounts by applying box filters of varying sizes to the integral image.
Frequency Domain Filtering
- Design Linear Filters in the Frequency Domain
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.