IMAGE MAPPING AND OBJECT REMOVAL USING DAUBECHIES WAVELET TRANSFORM IN IMAGE INPAINTING

Exemplar-based image inpainting technology is a “double-edged sword”. It can not only restore the integrity of image by inpainting damaged or removed regions, but can also tamper with the image by using the pixels around the object region to fill in the gaps left by object removal. Through the research and analysis, it is found that the existing exemplar-based image inpainting forensics methods generally have the following disadvantages: the abnormal similar patches are time-consuming and inaccurate to search, have a high false alarm rate and a lack of robustness to multiple post-processing combined operations. In view of the above shortcomings, a detection method based on long short-term memory (LSTM)-convolutional neural network (CNN) for image object removal is proposed. In this method, CNN is used to search for abnormal similar patches. Because of CNN’s strong learning ability, it improves the speed and accuracy of the search. The LSTM network is used to eliminate the influence of false alarm patches on detection results and reduce the false alarm rate. A filtering module is designed to eliminate the attack of post-processing operation. Experimental results show that the method has a high accuracy, and can resist the attack of post-processing combination operations. It can achieve a better performance than the state-of-the-art approaches.

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Artificial human face recognition via Daubechies wavelet transform and SVM

2011 16th International Conference on Computer Games (CGAMES) ◽

10.1109/cgames.2011.6000330 ◽

2011 ◽

Cited By ~ 5

Author(s):

Manel Boukhris ◽

Abdallah A. Mohamed ◽

Darryl D'Souza ◽

Marc Beck ◽

Najoua Essoukri Ben Amara ◽

...

Keyword(s):

Face Recognition ◽

Wavelet Transform ◽

Daubechies Wavelet ◽

Human Face ◽

Human Face Recognition

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Hand rotate EEG signal feature extraction by second order Daubechies wavelet transform (DWT)

10.1109/icccnt.2012.6396080 ◽

2012 ◽

Cited By ~ 1

Author(s):

S. Michahial ◽

R. Ranjith Kumar ◽

P. Hemath Kumar ◽

A. Puneeth Kumar

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Second Order ◽

Daubechies Wavelet ◽

Eeg Signal

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An image inpainting method for object removal based on difference degree constraint

Multimedia Tools and Applications ◽

10.1007/s11042-020-09835-0 ◽

2020 ◽

Author(s):

Lei Zhang ◽

Minhui Chang

Keyword(s):

Source Region ◽

Image Inpainting ◽

Mismatch Error ◽

Matching Rule ◽

Target Region ◽

Object Removal ◽

Degree Constraint ◽

Mean Differences ◽

Degree Of Similarity ◽

Restoration Effect

Abstract In the inpainting method for object removal, SSD (Sum of Squared Differences) is commonly used to measure the degree of similarity between the exemplar patch and the target patch, which has a very important impact on the restoration results. Although the matching rule is relatively simple, it is likely to lead to the occurrence of mismatch error. Even worse, the error may be accumulated along with the process continues. Finally some unexpected objects may be introduced into the target region, making the result unable to meet the requirements of visual consistency. In view of these problems, we propose an inpainting method for object removal based on difference degree constraint. Firstly, we define the MSD (Mean of Squared Differences) and use it to measure the degree of differences between corresponding pixels at known positions in the target patch and the exemplar patch. Secondly, we define the SMD (Square of Mean Differences) and use it to measure the degree of differences between the pixels at known positions in the target patch and the pixels at unknown positions in the exemplar patch. Thirdly, based on MSD and SMD, we define a new matching rule and use it to find the most similar exemplar patch in the source region. Finally, we use the exemplar patch to restore the target patch. Experimental results show that the proposed method can effectively prevent the occurrence of mismatch error and improve the restoration effect.

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