Structure and gradient‐based industrial interpolation using computational intelligence

Abstract

SummaryIndustry and medical imaging are technologies and processes of managing subjective representations of the industry product, interior of a body for analyzing. The deinterlacing is the procedure of transforming interlaced signal into progressive one. In this paper, I propose a new single field deinterlacing method which has three sub‐methods: bilinear method, small filter‐based method, and large filter‐based method. Pixels in a given image are grouped into three regions by calculated local mean and variance values: stable region, neutral region, and complex region. To implement deinterlacing process, I use weight average filter by considering two factors (the likeness factor and the distance factor) and determine region characteristics. Simulation results inform that the presented method yields satisfactory results in objective metrics (PSNR, SSIM, implementation time) and subjective quality.

Special Issue on Computational Intelligence Techniques for Industrial and Medical Applications

“…In the contribution by Jeon "Structure and gradient-based industrial interpolation using computational intelligence," the author proposes a new single field deinterlacing method which has three submethods: bilinear method, small filter-based method, and large filter-based method. 7 Pixels in a given image are grouped into three regions by calculated local mean and variance values: stable region, neutral region, and complex region. To implement deinterlacing process, the author used weight average filter by considering two factors (the likeness factor and the distance factor) and determined region characteristics.…”