Overview
Unsharp masking blurs an image, subtracts the blur to find detail, and adds scaled detail to the original image.
CoreSource checked
Unsharp Masking
Sharpens an image by adding back high-frequency detail separated by blur.
Quick Facts
Compact metadata for implementation and discovery.
- Category
- Image Processing
- Difficulty
- Beginner
- Family
- Image Enhancement
- Tier
- Core
- Status
- Verified
- Review
- Source Checked
- Sources
- 1
- Formulas
- 1
- Relationships
- 1
- Code example
- Ready
- Visualization
- Ready
Core Concepts
blurred imagedetail layersharpening amounthigh-frequency boost
Quick facts
Complexity
Runtime and memory behavior at a glance.
Best
O(pixels)
Average
O(pixels * kernel)
Worst
O(pixels * kernel)
Space
O(pixels)
When to Use
- Use when image enhancement needs blurred image.
- Use when the input matches the assumptions behind image Enhancement.
When Not to Use
- Avoid when image noise, lighting, or scale breaks the algorithm's assumptions.
- Avoid when a simpler algorithm gives the required result with less setup.
What It Does
- Sharpens an image by adding back high-frequency detail separated by blur
- Uses blurred image and detail layer to guide the work.
- Produces processed image.
Use Cases
- Image enhancement
- Feature extraction
- Segmentation and motion analysis
Inputs
- Image or image sequence
- Kernel, threshold, or model parameters
Outputs
- Processed image
- Mask
- Feature map or motion field
Key Formulas (1)
Unsharp mask
output = image + amount · (image − blur(image))
output = image + amount * (image - blur(image))Adds scaled high-frequency detail back to the image.
Pseudocode
- 1.Normalize the image and choose border handling
- 2.Evaluate the local filter, statistic, model, or response for each relevant pixel or region
- 3.Threshold, rank, refine, or combine the responses
- 4.Return the mask, transformed image, feature set, or geometric estimate
Canonical Example
- Input
- a small grayscale image patch with a clear edge, corner, or region
- Output
- Processed image
- Explanation
- Unsharp Masking applies blurred image to produce processed image.
Implementation Notes
- Handle borders and pixel coordinate conventions consistently.
- Keep the blurred image step separate enough to test independently.
- Keep coordinate conventions, border handling, and image normalization explicit in tests.
Common Pitfalls
- Applying operations in the wrong color space can distort the result.
- Test edge cases, not only clean examples.
- Thresholds tuned on one clean image can fail under different lighting, scale, blur, or noise.
Interactive Mechanism Walkthrough
Spatial Primitive & Sweep-Line CanvasShow the source image beside masks, filters, or motion vectors produced by the algorithm.
Step 1 of 4: Normalize the image and choose border handling
image-processing-mechanismNotes
Unsharp Masking should be used with the assumptions and tradeoffs of image Enhancement in mind.
Sources (1)
What each reference supports for this entry.
Digital Image Processing
BookImage-processing textbook support for Unsharp Masking's pixel-neighborhood operations, filtering rules, and image-analysis role.
Explore Next
Good next jumps from the relationship graph, ranked for useful discovery.
Relationships
View full relationship pageUsesGaussian Blur
Unsharp masking subtracts a blurred image to emphasize high-frequency detail.
Related Algorithms
Tags
Image ProcessingFilteringSmoothingEdge Enhancement