Depth Image Enhancement Algorithm Based on Fractional Differentiation

Depth image enhancement techniques can help to improve quality and facilitate computer vision tasks. Traditional image-enhancement methods, which are typically based on integer-order calculus, cannot exploit the textural information of an image, their effect is limited. To solve this problem, fractional differentiation has been introduced as innovative image-processing tool. It enables flexible use local non-local by taking into account continuous changes between orders, thereby improving effect. In study, a differential applied in depth used establish novel algorithm, named differential-inverse-distance-weighted method. Experiments performed verify effectiveness universality revealing that it effectively edge hole interference significantly enhance details. The effects order number iterations performance examined, optimal parameters obtained. process data associated with expected actual scenarios.