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 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.

A Hybrid Inpainting Model Combining Diffusion and Enhanced Exemplar Methods

2021 ◽  
Vol 13 (3) ◽  
pp. 1-19
Author(s):  
Sreelakshmy I. J. ◽  
Binsu C. Kovoor
Keyword(s):  
Patch Size ◽  
Image Inpainting ◽  
Qualitative Evaluation ◽  
Natural Images ◽  
External Information ◽  
Image Editing ◽  
Model Combining ◽  
Proposed Model ◽  
The World

Image inpainting is a technique in the world of image editing where missing portions of the image are estimated and filled with the help of available or external information. In the proposed model, a novel hybrid inpainting algorithm is implemented, which adds the benefits of a diffusion-based inpainting method to an enhanced exemplar algorithm. The structure part of the image is dealt with a diffusion-based method, followed by applying an adaptive patch size–based exemplar inpainting. Due to its hybrid nature, the proposed model exceeds the quality of output obtained by applying conventional methods individually. A new term, coefficient of smoothness, is introduced in the model, which is used in the computation of adaptive patch size for the enhanced exemplar method. An automatic mask generation module relieves the user from the burden of creating additional mask input. Quantitative and qualitative evaluation is performed on images from various datasets. The results provide a testimonial to the fact that the proposed model is faster in the case of smooth images. Moreover, the proposed model provides good quality results while inpainting natural images with both texture and structure regions.

Fullbore Image Construction of Imaging Logging Based on the Improved Texture Inpainting Method

2014 ◽  
Vol 571-572 ◽  
pp. 825-828
Author(s):  
Xiang Zhang ◽  
Jun Hua Wang ◽  
Xiao Ling Xiao
Keyword(s):  
Local Search ◽  
Texture Synthesis ◽  
Synthesis Method ◽  
Image Inpainting ◽  
Search Method ◽  
Local Search Method ◽  
Synthesis Algorithm ◽  
Image Construction ◽  
Tv Model ◽  
Improved Algorithm

The image inpainting method based on CriminiciA’s algorithm is slowly complete the image for large blank area. An improved algorithm based on the classic texture synthesis algorithm for image inpainting is proposed for imaging logging inpainting, which is used to generate the fullbore image. Two schemes, the local search method and priority calculation with TV model, are employed in the improved texture synthesis method. Some examples were given to demonstrate the effectiveness of the proposed algorithm on dealing with fullbore image construction with large blank area and raising efficiency obviously.

Background Noise Removal Technique using Deep Learning Segmentation Network without Segmentation Map

2021 ◽  
Vol 263 (2) ◽  
pp. 4441-4445
Author(s):  
Hyunsuk Huh ◽  
Seungchul Lee
Keyword(s):  
Deep Learning ◽  
Background Noise ◽  
Ground Truth ◽  
Noise Removal ◽  
Industrial Manufacturing ◽  
Proposed Model ◽  
Audio Data ◽  
Passive Noise ◽  
Removal Technique ◽  
Noise Canceling

Audio data acquired at industrial manufacturing sites often include unexpected background noise. Since the performance of data-driven models can be worse by background noise. Therefore, it is important to get rid of unwanted background noise. There are two main techniques for noise canceling in a traditional manner. One is Active Noise Canceling (ANC), which generates an inverted phase of the sound that we want to remove. The other is Passive Noise Canceling (PNC), which physically blocks the noise. However, these methods require large device size and expensive cost. Thus, we propose a deep learning-based noise canceling method. This technique was developed using audio imaging technique and deep learning segmentation network. However, the proposed model only needs the information on whether the audio contains noise or not. In other words, unlike the general segmentation technique, a pixel-wise ground truth segmentation map is not required for this method. We demonstrate to evaluate the separation using pump sound of MIMII dataset, which is open-source dataset.

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 adaptive Cahn-Hilliard equation for enhanced edges in binary image inpainting

2020 ◽  
Vol 14 ◽  
pp. 174830262094143
Author(s):  
Anis Theljani ◽  
Hamdi Houichet ◽  
Anis Mohamed
Keyword(s):  
Image Inpainting ◽  
Binary Image ◽  
Mesh Adaptation ◽  
Singular Set ◽  
Discrete Level ◽  
Diffusion Operator ◽  
Hilliard Equation ◽  
Proposed Model ◽  
Order Sets ◽  
Cahn Hilliard Equation

We consider the Cahn-Hilliard equation for solving the binary image inpainting problem with emphasis on the recovery of low-order sets (edges, corners) and enhanced edges. The model consists in solving a modified Cahn-Hilliard equation by weighting the diffusion operator with a function which will be selected locally and adaptively. The diffusivity selection is dynamically adopted at the discrete level using the residual error indicator. We combine the adaptive approach with a standard mesh adaptation technique in order to well approximate and recover the singular set of the solution. We give some numerical examples and comparisons with the classical Cahn-Hillard equation for different scenarios. The numerical results illustrate the effectiveness of the proposed model.

scholarly journals Quality Improvement for Exemplar-based Image Inpainting using a Modified Searching Mechanism

2020 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Mariwan Wahid Ahmed ◽  
Alan Anwer Abdulla
Keyword(s):  
Image Processing ◽  
Image Compression ◽  
Euclidean Distance ◽  
Image Inpainting ◽  
Input Image ◽  
Second Step ◽  
Research Areas ◽  
Potential Applications ◽  
Priority Function ◽  
Popular Images

Digital image processing has a significant impact in different research areas including medical image processing, biometrics, image inpainting, object detection, information hiding, and image compression. Image inpainting is a science of reconstructing damaged parts of digital images and filling-in regions in which information are missing which has many potential applications such as repairing scratched images, removing unwanted objects, filling missing area, and repairing old images. In this paper, an image inpainting algorithm is developed based on exemplar, which is one of the most important and popular images inpainting technique, to fill-in missing area that caused either by removing unwanted objects, by image compression, by scratching image, or by image transformation through internet. In general, image inpainting consists of two main steps: The first one is the priority function. In this step, the algorithm decides to select which patch has the highest priority to be filled at the first. The second step is the searching mechanism to find the most similar patch to the selected highest priority patch to be inpainted. This paper concerns the second step and an improved searching mechanism is proposed to select the most similar patch. The proposed approach entails three steps: (1) Euclidean distance is used to find the similarity between the highest priority patches which need to be inpainted with each patch of the input image, (2) the position/location distance between those two patches is calculated, and (3) the resulted value from the first step is summed with the resulted value obtained from the second step. These steps are repeated until the last patch from the input image is checked. Finally, the smallest distance value obtained in step 3 is selected as the most similar patch. Experimental results demonstrated that the proposed approach gained a higher quality in terms of both objectives and subjective compared to other existing algorithms.

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