An Adaptive Neuro-Fuzzy Inference System Modeling for Grid-Adaptive Interpolation over Depth Images

TL;DR

This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) to model grid-adaptive interpolation filters for depth images, aiming to minimize noise and delay in image processing.

Contribution

It presents a novel ANFIS-based approach to model and analyze grid-adaptive interpolation filters for depth images, combining fuzzy inference with neural network learning.

Findings

Effective modeling of grid-adaptive filters using ANFIS

Improved interpolation accuracy for depth images

Potential reduction in noise and delay

Abstract

A suitable interpolation method is essential to keep the noise level minimum along with the time-delay. In recent years, many different interpolation filters have been developed for instance H.264-6 tap filter, and AVS- 4 tap filter. The present work uses Adaptive Neuro-Fuzzy Inference System (ANFIS) technique to model and investigate the effects of a four-tap low-pass tap filter (Grid-adaptive filter) on a hole-filled depth image. The work demonstrates the general form of uniform interpolations for both integer and sub-pixel locations in terms of the sampling interval and filter length of depth-images via diverse finite impulse response filtering schemes. The demonstrated model combined modelling function of fuzzy inference with the learning ability of artificial neural network.