Analysis of Interpolation-Based Image In-Painting Approaches

Zor, Mustafa; Bostancı, Erkan; Güzel, Mehmet Serdar; Karataş, Erinç

doi:10.1201/9781003221333-8

Cited by 2 publications

(4 citation statements)

References 13 publications

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“…The optimal patch scale plays vital role in this in‐painting process. Evaluation metrics, like PSNR and elapsed time both are utilized to examine the performance of presented method as well as compared to the existing in‐painting techniques, like video in‐painting technique with enhanced priority function and image in painting algorithm (IIT‐EPF‐IIA) 20 video in‐painting technique with enhanced priority function and intelligent fast local image in‐painting techniques (IIT‐EPF‐IFLII), 8 video in‐painting technique with enhanced priority function and interpolation algorithm (IIT‐EPF‐IPA), 2 and video in‐painting technique with enhanced priority Function and four kriging (IIT‐EPF‐FK) 21 .…”

Section: Resultsmentioning

confidence: 99%

“…Zor et al, 2 have presented the interpolation algorithms used in image in‐painting. Errors and noise generated on the color and gray scale formats of generally utilized standard images were corrected by cubic, kriging, radial based function and high dimensional model representation approaches and the results were compared using standard image comparison criteria, namely, PSNR, SSIM, MSE.…”

Section: Literature Survey: a Brief Reviewmentioning

confidence: 99%

“…From the pixels close to the area boundary normally, image or video in‐painting reproduces the region of chosen image. From the image processing network, the entire data has gained much notoriety and to finish static image's missing parts along encompassing image in‐painting procedure is utilized 2 . As an image in‐painting algorithms natural extension, the video in‐painting is begun off.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MABC‐EPF: Video in‐painting technique with enhanced priority function and optimal patch search algorithm

Rao

Chakrapani²,

Kumar

2022

Concurrency and Computation

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In this article, video in-painting technique with enhanced priority function and optimal patch searching algorithm is proposed. Initially, the source frames are extracted from video based on edge information, these frames are partitioned into two regions (1) target region (region to be in-painted) and (2) source region that is region to be considered as appropriate patch selection for in-painting the target region. A new priority function using confidence value, local structure multiplier, and motion vector uniformity to determine the in-paint location. The suitable patch is identified in source region, using correlation measure, like sum of squared difference method. The searching of the suitable patch in source region is optimized through modified artificial bee colony (MABC) algorithm. To capture more edge information, irregular patch filling is carried out to in-painting process. The in-painting process is more improved by optimal patch scaling for better vision. Finally, the in-painted source frames are combined to obtain the corrected video. The proposed method works better than the other art state applications.The proposed peak signal to noise ratio (PSNR) attains better 8.06%, 7.90%, 32.15%, and 13.06% compared with other previous methods.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Survey: a Brief Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MABC‐EPF: Video in‐painting technique with enhanced priority function and optimal patch search algorithm

Rao

Chakrapani²,

Kumar

2022

Concurrency and Computation

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show abstract

“…Some popular image interpolation methods are nearest neighbour interpolation, Bilinear interpolation, Bicubic interpolation and Kriging interpolation [10]. All these techniques have its own corresponding equations and the computational complexity of these equations makes it really challenging in image inpainting applications [11].…”

Section: Introductionmentioning

confidence: 99%

Reconv: Repeated Convolutional Strategies for Advanced Image Inpainting

Jini.P,

K.K

2024

Preprint

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Image inpainting is a promising but challenging approach that fills in huge free-form empty areas in images. Most of the recent papers concentrate on splitting masked image into two matrices of valid and invalid elements which makes the system more complex. This paper proposes a novel algorithm named ReConv which uses a repeated standard convolution operation which treats valid and invalid elements of an image in the same manner. The outcomes of our suggested method, ReConv, shows that, in comparison to earlier approaches, our system produces outputs that are more adaptable with good quality for real world applications. Our suggested technique enables users quickly modify faces, eliminate distracting items, change image layouts, and remove unwanted text. An extensive comparison study on two types of datasets validates our method. The effectiveness of the suggested strategy was evaluated using different measures such as PSNR, SSIM and FID. The results show that our recommended approach excels in performance compared to the existing modern methods.

show abstract

Analysis of Interpolation-Based Image In-Painting Approaches

Cited by 2 publications

References 13 publications

MABC‐EPF: Video in‐painting technique with enhanced priority function and optimal patch search algorithm

MABC‐EPF: Video in‐painting technique with enhanced priority function and optimal patch search algorithm

Reconv: Repeated Convolutional Strategies for Advanced Image Inpainting

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