Gradient
Highlights changes in color or intensity within an image by accentuating transitions and edges.
🖼️ Image options and parameters of gradient method
Gradient filter or specifically a gradient-based edge detection filter, is an image processing technique used to highlight edges and boundaries within an image by emphasizing areas of rapid intensity change. The gradient filter operates by calculating the rate of change of pixel intensities across the image. When there's a rapid transition from one intensity level to another, the convolution operation captures this change as a high gradient magnitude value, indicating the presence of an edge. It's a fundamental step in various computer vision and image analysis tasks, such as edge detection, object recognition, and image segmentation.
The gradient filter enhances edges by detecting abrupt changes in pixel intensities.
Keep in mind that gradient filters can be sensitive to noise and might result in false edges or emphasize noise. Smoothing the image (e.g., using Gaussian blur) before applying the gradient filter can help mitigate this issue.
Kinds of images compatible with algorithm
| Image property | What it means | Possible values |
|---|---|---|
bitDepth | number of bits per channel | [8,16] |
components | number of components | [1] |
alpha | is alpha channel allowed | true |
Parameters and default values
options
Options
| Property | Required | Default value |
|---|---|---|
kernelX | * | - |
kernelY | * | - |
bitDepth | no | image.bitDepth |
borderType | no | replicate |
borderValue | no | 0 |
* - if applying filter is necessary in only one of directions, then a user can pass one kernel instead of two. However, if none were passed on, function will throw an error.
Implementation
Here's how gradient filter is implemented in ImageJS:
Grayscale Conversion: Before applying a gradient filter, the color image is converted into grayscale. This simplifies the processing by reducing the image to a single channel representing pixel intensities.
Kernel Operators: Gradient filter consists of small convolution kernels. Normally, one for detecting horizontal changes and another for vertical changes, however user might indicate only one kernel to check only one of directions. These kernels are usually 3x3 matrices of numerical weights.
Convolution Operation: The gradient filter is applied through a convolution operation, where the filter kernel slides over the grayscale image. At each position, the convolution operation involves element-wise multiplication of the filter kernel with the corresponding pixels in the image, followed by summing up the results. This sum represents the rate of intensity change (gradient) at that location in the image.
Gradient Magnitude and Direction: For each pixel, the gradient magnitude is calculated by combining the results of the horizontal and vertical convolutions. The corresponding values from each convolution are put in square and summed, then put in square root.
Edge Detection: The gradient magnitude values are used to identify regions of rapid intensity change, which correspond to edges in the image. Higher gradient magnitude values indicate stronger edges.
Thresholding: To further refine the edges detected, a thresholding step is often applied. Pixels with gradient magnitudes below a certain threshold are considered as non-edges, while those above the threshold are considered edges. This helps in reducing noise and emphasizing significant edges.