scholarly journals Modified Region Filling and Object Removal by Exemplar - based Image Inpainting

2018 ◽  
Vol 182 (24) ◽  
pp. 27-31
Author(s):  
Ankit D. ◽  
Hetal M. ◽  
Ankurkumar G.
Keyword(s):  
Image Inpainting ◽  
Object Removal ◽  
Region Filling

Region Filling and Object Removal by Exemplar-Based Image Inpainting

2004 ◽  
Vol 13 (9) ◽  
pp. 1200-1212 ◽  
Author(s):  
A. Criminisi ◽  
P. Perez ◽  
K. Toyama
Keyword(s):  
Image Inpainting ◽  
Object Removal ◽  
Region Filling

Object Removal and Region Filling in Image

2016 ◽  
Author(s):  
Pankaj Bhaurao Bagal ◽  
Ravi Kateeyare
Keyword(s):  
Object Removal ◽  
Region Filling

scholarly journals A Detection Approach Using LSTM-CNN for Object Removal Caused by Exemplar-Based Image Inpainting

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 858
Author(s):  
Ming Lu ◽  
Shaozhang Niu
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Short Term Memory ◽  
Image Inpainting ◽  
Learning Ability ◽  
Post Processing ◽  
Object Removal ◽  
Combined Operations ◽  
Detection Approach ◽  
Lstm Network

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.

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

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.

Patch-Based Inpainting for Object Removal and Region Filling in Images

2013 ◽  
Vol 22 (3) ◽  
pp. 335-350 ◽  
Author(s):  
Rajesh Pandurang Borole ◽  
Sanjiv Vedu Bonde
Keyword(s):  
Texture Synthesis ◽  
Target Region ◽  
New Approach ◽  
Structure Information ◽  
The Core ◽  
Search Area ◽  
Object Removal ◽  
Execution Speed ◽  
Small Cracks ◽  
Region Filling

AbstractA large number of articles have been devoted to the application of “texture synthesis” for large regions and “inpainting” algorithms for small cracks in an image. A new approach that allows the simultaneous filling in of different structures and textures is discussed in this present study. The combination of structure inpainting and patch-based texture synthesis carried out (termed as “patch-based inpainting”) for filling and updating the target region shows additional advantages over earlier approaches. The algorithm discussed here uses the patch-based inpainting with isophote-driven patch-based texture synthesis at the core. In this algorithm, once the user selects the regions to be restored, the algorithm automatically searches and fills in these regions with the best matching information surrounding them. We have assigned high priorities to the pixels on the boundary and the structure by computing data terms D(p), and the texture and corners are prioritized by computing the confidence C(p) of the pixel. We also regularized and weighted the confidence of the pixels, RC(p), to achieve a balance of the two. The patch search area near the pixel patch to be filled is bounded for algorithm speed improvement. Patch-based filling significantly improve execution speed compared with pixel-based filling. Filling in is done in such a way that the structure information arriving at the region boundaries is propagated inside. A number of examples on real and synthetic images are used to demonstrate the effectiveness of the algorithm. Robustness with respect to the shape of the selected target region is also demonstrated.

Robust object removal with an exemplar-based image inpainting approach

2014 ◽  
Vol 123 ◽  
pp. 150-155 ◽  
Author(s):  
Jing Wang ◽  
Ke Lu ◽  
Daru Pan ◽  
Ning He ◽  
Bing-kun Bao
Keyword(s):  
Image Inpainting ◽  
Object Removal
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