Journal ArticleDOI
Bayes-Probabilistic-Based Fusion Method for Image Inpainting
Manjunath R. Hudagi,Shridevi Soma,Rajkumar L. Biradar +2 more
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TLDR
This paper proposes an effective hybrid image inpainting method that is termed as ALGDKH, which is the hybridization of Ant Lion–Gray Wolf Optimizer (ALG)-based Markov random field (MRF) modeling, deep learning, [Formula: see text]-nearest neighbors (KNN) and the harmonic functions.Abstract:
Image inpainting removes unwanted objects from the image, signifying the original image restoration. Even though several techniques are introduced for image inpainting, but still, there are several challenging issues associated with the conventional methods regarding data loss, which are effectively handled based on the proposed approach. In this paper, we propose an effective hybrid image inpainting method that is termed as ALGDKH, which is the hybridization of Ant Lion–Gray Wolf Optimizer (ALG)-based Markov random field (MRF) modeling, deep learning, [Formula: see text]-nearest neighbors (KNN) and the harmonic functions. The crack input image is forwarded as an input to Markov random field modeling to obtain image inpainting, where the MRF energy is minimized based on the ALG. Then, the same crack image is subjected to the Whale–MBO-based DCNN, KNN with Bhattacharya distance and Bi-harmonic function modules to obtain the inpainting results. Finally, the results from the proposed ALG-based Markov random field modeling, Whale–MBO-based DCNN, KNN with Bhattacharya distance and Bi-harmonic function modules are fused through Bayes-probabilistic fusion for the final inpainting results. The proposed method produces the maximal PSNR of 38.14[Formula: see text]dB, maximal SDME of 75.70[Formula: see text]dB and the maximal SSIM of 0.983.read more
Citations
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Journal ArticleDOI
SR-DeblurUGAN: An End-to-End Super-Resolution and Deblurring Model with High Performance
TL;DR: A weighted perceptual loss-based generative adversarial network is used to deblur the UAV images, which removes the blur and restores the texture details of the images well.
Journal ArticleDOI
Political Improved Invasive Weed Optimization-Driven Hybrid Exemplar Technique for Video Inpainting Process
TL;DR: Wang et al. as mentioned in this paper designed a political improved invasive weed optimization (PIIWO)-based optimal exemplar for the productive video inpainting process, which is newly designed by combining political optimizer and Improved Invasive Weed Optimization.
References
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Journal ArticleDOI
Grey Wolf Optimizer
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
Journal ArticleDOI
The Whale Optimization Algorithm
Seyedali Mirjalili,Andrew Lewis +1 more
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
Journal ArticleDOI
The Ant Lion Optimizer
TL;DR: The results of the test functions prove that the proposed ALO algorithm is able to provide very competitive results in terms of improved exploration, local optima avoidance, exploitation, and convergence, showing that this algorithm has merits in solving constrained problems with diverse search spaces.
Proceedings ArticleDOI
Navier-stokes, fluid dynamics, and image and video inpainting
TL;DR: A class of automated methods for digital inpainting using ideas from classical fluid dynamics to propagate isophote lines continuously from the exterior into the region to be inpainted is introduced.
Journal ArticleDOI
Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA)
TL;DR: A novel inpainting algorithm that is capable of filling in holes in overlapping texture and cartoon image layers using a direct extension of a recently developed sparse-representation-based image decomposition method called MCA (morphological component analysis).