sharpnessadjustment

Sharpness adjustment is an image processing operation that increases perceived detail in a digital image by enhancing edge contrast and high-frequency content. It is used to counteract soft focus, optical blur, or downsampling artifacts and is a standard feature in modern photo editors, scanning software, and printing pipelines. Effective sharpening depends on image content, resolution, and output medium.

Algorithms for sharpness adjustment fall into several families. Unsharp masking creates a blurred version of the image, subtracts it to form a mask, and then adds a scaled copy back to the original to intensify edges. Parameters commonly include amount (strength), radius (edge width), and threshold (minimum difference required to sharpen). High-pass filtering preserves detail by isolating high-frequency components and adding them back to the image, often with a blending factor. Deconvolution methods attempt to reverse blur by estimating the blur kernel and iteratively restoring sharpness, but they require accurate blur models and can amplify noise.

Practical sharpening requires balance to avoid artifacts such as halos, oversharpening, and noise amplification. It is typically adjusted after resizing and noise reduction, and many workflows apply sharpening selectively with masks to protect flat or smooth areas.

Historically, the term sharpness adjustment has been used across software interfaces to describe sharpening tools; the unsharp masking technique is widely used and sometimes misnamed, despite not removing blur with true unsharpness.