# Image Filtering

Convolution and correlation, predefined and custom filters, nonlinear
filtering, edge-preserving filters

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.

## Apps

Image Region Analyzer | Browse and filter connected components in image |

## Functions

## Topics

### 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.

### Denoising Filtering

**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.

### Edge-Preserving Filtering

**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.**Specify Desired 2-D Frequency Response Matrix**

The 2-D filter design functions`fsamp2`

,`fwind1`

, and`fwind2`

all create filters based on a desired frequency response magnitude matrix.**Calculate the Frequency Response of a 2-D Filter**

Calculate and display the frequency response of a two-dimensional filter using the`freqz2`

function.