A Novel Thanka Image Inpainting Method with Euler’s Elastica and Iterative Denoising and Backward Projections

Abstract

This paper presents a brand-new Thanka picture inpainting technique based on Euler’s elastica, iterative denoising, and backward projections (EEIDBP). Specifically, a model of Euler’s elastica is introduced to estimate the original observation due to its lower staircasing effects and better approximation of natural images. A method for backward projection and iterative denoising is applied to achieve a more accurate estimate of the original signal by alternating iterations between the estimation of the original signal and the estimation of the original observation. The experimental findings demonstrate that, in terms of a subjective assessment, the quantitative peak signal-to-noise ratio (PSNR), and the structural similarity (SSIM), the proposed technique outperforms the state-of-the-art picture inpainting methods.