How to Use the imageconvolution Function to Create Emboss and Shadow Effects? A Comparative Analysis

In PHP image processing, the imageconvolution function is a powerful tool for applying custom filter effects.

• $image: The image resource to process.
• $matrix: A 3x3 convolution matrix that defines the filter effect.
• $div: The sum of all matrix values, used as a normalization factor.
• $offset: An offset added to each color channel after calculation, used to adjust brightness.

2. Emboss Effect Implementation

Emboss effects typically enhance image edges and introduce a simulated light source to create a three-dimensional feel. Below is a common convolution matrix for embossing:

$embossMatrix = [
    [-<span class="hljs-number">2, -1, 0],
    [-1,  1, 1],
    [ 0,  1, 2]
];

This matrix simulates lighting from the top-left corner, creating a sense of depth. Example implementation:

$img = imagecreatefromjpeg('example.jpg');
imageconvolution($img, $embossMatrix, 1, 128);
imagejpeg($img, 'embossed.jpg');
imagedestroy($img);

3. Shadow Effect Implementation

Shadow effects are commonly used to enhance depth or highlight subjects. The following is a convolution matrix that simulates a shadow by applying blur and slight offset:

$shadowMatrix = [
    [1, 1, 1],
    [1, 0, 1],
    [1, 1, 1]
];

This matrix performs a mild blur on the image. To make it more like a shadow, it’s often combined with transparency overlays or layer offsets. Here's how to use it:

$img = imagecreatefrompng('logo.png');
imageconvolution($img, $shadowMatrix, 8, 0);
imagepng($img, 'shadowed.png');
imagedestroy($img);

4. Comparison of Emboss and Shadow Effects

5. Summary

imageconvolution offers flexible capabilities for image processing. By designing different convolution matrices, we can achieve a wide range of visual effects. Embossing is ideal for highlighting structural lines in images, while shadow effects help emphasize visual layering and focal points. In practical applications, understanding and combining these basic effects is a crucial step toward advanced image processing.