What are filters in image processing?

Filters in image processing are matrices or kernels convolved over the images to modify their appearance or extract their specific features. Filters work by modifying an image's pixel values based on neighboring pixels' values.

There are several types of filters in image processing:

Smoothing filters

These filters reduce noise from the images by blurring them with respect to their neighborhood pixels. Here are some of the smoothing filters:

• Gaussian filter: This filter applies a two-dimensional Gaussian function to the neighborhood pixels to smoothen the image. The greater the standard deviation of the Gaussian distribution, the greater the blur will be.

Note: Read more about the Gaussian filter.

• Median filter: This filter replaces each pixel value with the median of the neighboring pixels. It is effective in reducing the salt and pepper noise from the images.

Note: Read more about the median filter.

Sharpening filters

These filters enhance an image's details and edges, making them more pronounced. Here are some of the sharpening filters:

• Laplacian filter: This filter convolves over the image based on the principle of the Laplace transform. It calculates the image matrix's second-order derivative and highlights its edges and details by emphasizing regions of rapid intensity changes.

Edge detection filters

• Sobel filter: It detects the edges by calculating the horizontal and vertical derivatives of the image and then combining them.

• Robert filter: It detects the edges by calculating and combining derivatives of both the image diagonals.

Thresholding filters

• Binary threshold filter: This filter converts a greyscaled image into a binary image by setting pixel values above a threshold to white and values below the threshold to black.

• Adaptive threshold filter: It is similar to the binary threshold filter, but it determines its threshold based on the local neighborhood of each pixel.

Morphological filters

These filters are widely used in morphological image processing. Following are a few morphological filters:

• Dilation filter: This filter expands the boundaries of regions in an image by replacing each pixel with a maximum value in its neighborhood. It helps fill gaps, join broken lines, and enlarge objects.

• Erosion filter: This filter shrinks the boundaries of regions by replacing each pixel with the minimum value with its neighborhood. It helps remove noise, separates connected objects, and reduces object size.

Conclusion

The filters mentioned above represent only a subset of the wide range of filters available in image processing. Each filter serves a specific purpose and can be applied based on the desired outcome. Furthermore, deep learning models can be leveraged to identify the most suitable filter for a specific task or objective.