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.

scholarly journals Object Removal using a New Exemplar-Based Image Inpainting Algorithm and Seam Carving

2019 ◽  
Vol 8 (4) ◽  
pp. 9186-9191
Keyword(s):  
Texture Synthesis ◽  
Image Inpainting ◽  
Seam Carving ◽  
Target Region ◽  
Object Removal ◽  
Proposed Model ◽  
Priority Function ◽  
Removal Technique

This paper proposed a new object removal techniques based on exemplar-based technique and seam carving. Exemplar- based inpainting technique has been a point of attraction due to its moderate computational task an its performance. Moreover in this paper object removal technique for image based on discontinuous by seam carving has been introduced. Image inpainting is an approach fro restoring the damage part of an image in reference to the information from the undamaged part to make the restored image continuous, natural and to look complete. In this method patches are used to fill the target region in the image. Both texture synthesis and structure propagation are used simultaneously. Here robust exemplar method has been used avoiding dropping effect by using robust priority function. We have also used seam-carving for object removal. The PSNR values of the proposed model have been calculated and is also compared with the previous techniques’ results.

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 Image Inpainting Based on Multi-Patch Match with Adaptive Size

2020 ◽  
Vol 10 (14) ◽  
pp. 4921 ◽  
Author(s):  
Shiyuan Yang ◽  
Haitao Liang ◽  
Yi Wang ◽  
Huaiyu Cai ◽  
Xiaodong Chen
Keyword(s):  
Patch Size ◽  
Source Region ◽  
Image Inpainting ◽  
Experimental Results ◽  
Multiple Target ◽  
Fixed Size ◽  
Single Target ◽  
Low Efficiency

Patch-based image inpainting methods iteratively fill the missing region via searching the best sample patch from the source region. However, most of the existing approaches basically use the fixed size of patch regardless of content features nearby, which may lead to inpainting defects. Also, global match is needed for searching the best sample patch, but only to fill one target patch in each iteration, resulting in low efficiency. To handle the issues above, we first evaluate the nonuniformity in an image, by which the patch size is adaptively determined. Moreover, we divide the source region into multiple non-overlapping subregions with different nonuniformity levels, and the patch match proceeds in every subregion, respectively. This strategy not only saves the match time for single target patch, but also reduces the mismatch, and enables the simultaneous filling of multiple target patches in a single iteration. Experimental results show that in comparison to previous patch-based works, our method has achieved further improvement both in quality and efficiency. We believe our method could provide a new way for patch match with better accuracy and efficiency in image inpainting tasks.

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

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

scholarly journals A novel image inpainting framework based on multilevel image pyramids

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Md. Salman Bombaywala ◽  
Chirag Paunwala
Keyword(s):  
Search Strategy ◽  
Image Inpainting ◽  
Search Space ◽  
Human Vision ◽  
Image Pyramid ◽  
Target Region ◽  
Image Pyramids ◽  
Multi Level ◽  
Coherent Information ◽  
State Of Art

Image inpainting is the art of manipulating an image so that it is visually unrecognizable way. A considerable amount of research has been done in this area over the last few years. However, the state of art techniques does suffer from computational complexities and plausible results. This paper proposes a multi-level image pyramid-based image inpainting algorithm. The image inpainting algorithm starts with the coarsest level of the image pyramid and overpainting information is transferred to the subsequent levels until the bottom level gets inpainted. The search strategy used in the algorithm is based on hashing the coherent information in an image which makes the search fast and accurate. Also, the search space is constrained based on the propagated information thereby reducing the complexity of the algorithm. Compared to other inpainting methods; the proposed algorithm inpaints the target region with better plausibility and human vision conformation. Experimental results show that the proposed algorithm achieves better results as compared to other inpainting techniques.

Efficient Image Inpainting Using Dimensional Space Reduction via Adaptive Patch-based Concept and Rank Lowering Technique

2021 ◽  
Vol 30 (03) ◽  
pp. 2150014
Author(s):  
Kimia Peyvandi ◽  
Farzin Yaghmaee
Keyword(s):  
Dimensional Space ◽  
Image Inpainting ◽  
Feature Space ◽  
Low Rank ◽  
Target Region ◽  
Original Space ◽  
Space Reduction ◽  
Quad Tree ◽  
New Space ◽  
Low Dimensional

In this paper, we present a new algorithm for image inpainting using low dimensional feature space. In our method, projecting a low dimensional space from the original space is accomplished firstly using SVD, which is named low rank component, and then the missing pixels are filled in the new space. Finally, the original image is inpainted so that adaptive patch size is considered by quad-tree based on the previous step. In our algorithm, the missing pixels in the target region are estimated twice, one in low dimension feature space and another in the original space. It is noticeable that both processes estimate the unknown pixels using patch-based idea and rank lowering concept. Experimental results of this algorithm show better consistency in comparison with state-of-the-art methods.

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