higherboost

Higherboost, commonly referred to as high-boost filtering, is a sharpening technique used in image and signal processing to enhance fine details while preserving the overall structure of the image. It achieves this by emphasizing high-frequency components that carry edge and texture information.

The method works by combining the original signal with a boosted version of its high-frequency content. A typical implementation computes a blurred version of the image to approximate the low-frequency component, subtracts this blur to obtain the high-frequency details, and then adds a scaled amount of those details back to the original image. A common formulation is g = f + β (f − f_blur), where f is the original image, f_blur is a blurred version, and β > 0 is the boost factor. Equivalently, g = (1 + β) f − β f_blur. The boost factor controls how strongly edges and textures are amplified.

High-boost filtering is related to unsharp masking; unsharp masking can be viewed as a special case with a particular boost value. The technique can use various blur kernels (Gaussian, box, etc.), and different implementations may adjust the balance between sharpening strength and noise amplification.

Applications include photographic sharpening, enhancement of medical and satellite imagery, and video processing where edge definition is important. Limitations include the amplification of image noise and artifacts, potential haloing around edges, and the need to select appropriate blur radius and boost factor. In practice, higherboost is implemented in many image processing libraries as a configurable sharpening option, sometimes via a dedicated high-boost kernel or through the f + β(f − f_blur) formulation.