Aliasing is a visual artifact that occurs when a digital camera sensor cannot accurately represent fine detail or repeating patterns in a scene. It most commonly appears as jagged, stair-stepped edges on diagonal lines and curves, or as wavy color bands called moire patterns on textured surfaces like fabric weaves, brick walls, or window screens.
What Causes Aliasing
Digital sensors capture images using a grid of individual pixels. Each pixel records a single color value for its location. When a scene contains details that are finer than the spacing of the pixel grid, the sensor cannot represent them accurately. Instead, it produces false patterns that were not present in the original scene. A set of thin parallel lines, for example, might appear to shimmer or create wavy interference bands. A sharp diagonal edge might appear as a series of tiny steps rather than a smooth line.
Types of Aliasing
Spatial aliasing produces the jagged, stair-stepped edges commonly called “jaggies.” This is most visible on high-contrast diagonal lines, such as the edge of a building against the sky or text rendered on screen. Moire is a form of aliasing that appears when repeating patterns in the subject interact with the repeating pattern of the pixel grid. It shows up as colorful wavy bands or false textures, often visible on woven fabrics, tiled roofs, or fine architectural details.
How to Reduce Aliasing
Most cameras include an anti-aliasing filter (optical low-pass filter) in front of the sensor. This filter slightly softens the image at the pixel level, preventing the finest details from creating false patterns. The trade-off is a small reduction in absolute sharpness. Some cameras omit this filter to maximize detail, relying instead on higher pixel counts and software correction to manage aliasing.
In post-processing, aliasing can be partially reduced using sharpening algorithms that smooth jagged edges, and moire can be targeted with dedicated removal tools found in most RAW editors. Shooting at higher resolutions also reduces aliasing because finer pixel spacing can resolve more detail before artifacts appear. Understanding aliasing helps you anticipate when it might occur and take preventive steps, whether through camera choice, filter use, or careful attention to problematic subjects in the field.