Journal
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Volume -, Issue -, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218001423540149
Keywords
Euler's elastica energy; Thanka image inpainting; EEIDBP
Categories
Ask authors/readers for more resources
This paper presents a brand-new technique for Thanka picture inpainting, which is based on Euler's elastica, iterative denoising, and backward projections (EEIDBP). The technique introduces Euler's elastica model to estimate the original observation with lower staircasing effects and better approximation of natural images. Backward projection and iterative denoising are applied to achieve a more accurate estimate of the original signal. Experimental results demonstrate that the proposed technique outperforms state-of-the-art picture inpainting methods in subjective assessment, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM).
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available