Image Inpainting Process Research Articles

An efficient image inpainting algorithm based on a modified Gray–Scott model

In this paper, we proposes an image inpainting algorithm based on the modified Gray–Scott (GS) model. We have added a fidelity term to control the edge and stability of pixel value evolution during the image inpainting process. We verify the effectiveness and robustness of the proposed model through various experiments. By constructing binary images and complex pixel images with a bit depth of 8, we demonstrate the effectiveness of the model on damaged images with Gaussian noise and locally missing pixels. Additionally, we use metrics such as root mean square error (RMSE) and peak signal-to-noise ratio (PSNR) to evaluate the modified GS model. We compared the model with other methods in terms of both visual and computational metrics, and our proposed model outperforms other methods.

Optimization enabled deep learning approach with probabilistic fusion for image inpainting

ABSTRACT Nowadays, various deep learning (DL) approaches have been devised for image inpainting, which provided a substantial improvement in image quality. However, these approaches have failed to reconstruct the accurate structure of the original image. Hence, this research devised a novel and effective image inpainting approach, namely Autoregressive Flower Pollination Student Psychology Optimization (ArFPSPO). The image inpainting is carried out based on a newly devised hybrid context DL with the hybrid optimization scheme. The designed hybrid context DL approach adopts three DL techniques, such as Context encoder (CE), Context-Conditional Generative Adversarial Networks (CC-GAN), and Partial convolutional layer to complete the image inpainting process so that the outcomes are fused using probabilistic fusion with maximum entropy, thereby the final inpainted image is attained. Each of these three DL techniques is separately trained using a developed hybrid optimization technique. The experimental outcome reveals that the devised model gives optimal performance.

A Study of Iconographic Representations on Ratneswar Temple, Bhattabati and Digital Restoration through Image Inpainting Process

A Study of Iconographic Representations on Ratneswar Temple, Bhattabati and Digital Restoration through Image Inpainting Process

Research into an Image Inpainting Algorithm via Multilevel Attention Progression Mechanism

Existing image inpainting schemes generally have the problems of structural disorder and blurred texture details. This is mainly because, in the reconstruction process of the damaged area of the image, it is difficult for the inpainting network to make full use of the information in the nondamaged area to accurately infer the content of the damaged area. Therefore, the paper has proposed an image inpainting network driven by multilevel attention progression mechanism. The proposed network has compressed the high-level features extracted from the full-resolution image into multiscale compact features and then drives the compact features to perform multilevel order according to the scale size. Attention feature progression is to achieve the goal of the full progression of high-level features including structure and details in the network. To further realize fine-grained image inpainting and reconstruction, the paper has also proposed a composite granular discriminator to achieve image inpainting process performing global semantic constraints and nonspecific local dense constraints. The related experimental results in the paper can show that the proposed method can achieve higher quality repair results than state-of-the-art ones.

A deep neural network based framework for restoring the damaged persian pottery via digital inpainting

Conservation of the historical artworks involves carefully planning interventions to prolong the material, historical, and design integrity of humanity's-built heritage. Among which, restoration of the destroyed parts of the artworks via inpainting is one of the most critical stages in the restorative conservation.Indeed, one of the significant assets of any art-historical work is its aesthetic aspects. However, usually due to the visual complexity and the diversity of the motifs, the number of patterns that a restorer artist can have in mind when repairing them, are limited. Also, the inpainting process of such artworks might be very slow and prone to errors; a slight error can cause severe mistakes for the aesthetic aspects of the artifact. Therefore, the need to develop an intelligent system to provide an image of the refurbished artifact before to the restoration operation, is undeniable. Motivated by the idea that the computer-vision-based image inpainting process could be an efficient solution for this purpose, in this paper, for the first time, we propose a framework that employs a deep artificial neural network-based approach to inpaint the images of Persian pottery for restoring their damaged parts. The framework can repair irregular structures that usually exist in Persian pottery and inpaints relatively large missing parts. Besides, the restored parts were seamlessly integrated with the rest of the image without the need for any further postprocessing. To evaluate the proposed framework, we have collected 677 images from different Kubachi ware (known as Persian pottery) to study the inpainting results. The provided dataset is available free for the research community. Quantitative and qualitative evaluations of the results show that the proposed framework performs well, where the inpainted parts are visually and semantically plausible, and the details of the predicted colors in the damaged areas are very satisfying.

Analysis and Implementation of Enhanced Image Inpainting method using adjustable patch sizes

Image inpainting is the process of reconstruction of the damaged image and removal of unwanted objects in an image. In the image inpainting process patch priority andselection of best patch playsa major role. The patch size is also considered for producing good results in the image inpainting. In this paper patch priority is obtained by introducing a regularization factor (ɷ). The best patch selection is acquired by using the Sum of Absolute Difference (SAD) distance method. The results of inpainting are investigated with adjustable patch sizes of 5×5, 7×7, 9×9, 11×11, and 13×13 for the proposed method. The performance of these adjustable patch sizes is observed by using Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The best suitable patch size for good inpainting is announced based on the values of PSNR and MSE.

Lattice Boltzmann method for fractional Cahn-Hilliard equation

Fractional phase field models have been reported to suitably describe the anomalous two-phase transport in heterogeneous porous media, evolution of structural damage, and image inpainting process. It is commonly different to derive their analytical solutions, and the numerical solution to these fractional models is an attractive work. As one of the popular fractional phase-field models, in this paper we propose a fresh lattice Boltzmann (LB) method for the fractional Cahn-Hilliard equation. To this end, we first transform the fractional Cahn-Hilliard equation into the standard one based on the Caputo derivative. Then the modified equilibrium distribution function and proper source term are incorporated into the LB method in order to recover the targeting equation. Several numerical experiments, including the circular disk, quadrate interface, droplet coalescence and spinodal decomposition, are carried out to validate the present LB method. It is shown that the numerical results at different fractional orders agree well with the analytical solution or some available results. Besides, it is found that increasing the fractional order promotes a faster evolution of phase interface in accordance with its physical definition, and also the system energy predicted by the present LB method conforms to the energy dissipation law.

The image inpainting algorithm based on pruning samples by referring to four-domains

ABSTRACTIn order to retrieve large scale damaged image with rich geometry structure and texture information, the novel image inpainting algorithm based on the neighbourhood reference priority can not only maintain image character but also improve image inpainting speed has been proposed in the paper. The problems of an image inpainting process can be translated into the best sample searching process. First, the extracting structure information of image and the dividing sample region into sub-regions server. Second, in order to adjust the neglect of structure of matching method named SSD, introducing structure symmetry matching constraint into matching method, it avoids matching mistakenly and improves sample matching precision and searching efficiency. Then, improving priority equations by bringing in structure weight and confidence, highlighted the effect of structure to inpainting sequence. Finally, computing priority of four-domains neighbour by computation overlapping information between object patch and sample patch, so that referring to secure information of four-domains neighbour, to prune sample dataset and search optimal sample. The experimental results have demonstrated that the proposed algorithm can overcome problems like texture blurring and structure dislocations and so on, the PSNR of the improved algorithm has been increased by 0.5–1 dB comparing other contrast methods while speeding up the image inpainting process, recovered image is much continuous for visuality. Meanwhile, it can recover efficiently common damage images and be more pervasive.

Study on Digital Image Inpainting Method Based on Multispectral Image Decomposition Synthesis

The paper analyzes the image inpainting problem of damaged Painting Arts for high fidelity images reproduction, and a digital image inpainting method based on multispectral image decomposition synthesis is proposed. Firstly, multi-channel images of Painting Arts are obtained by multispectral technology. Then, a polynomial regression method based on principal component is used to reconstruct the spectral image. The reconstructed image is decomposed by VO image decomposition model. During the inpainting process, the channel correlation of the structure image and the texture image of multispectral image is effectively removed. The digital image inpainting is performed respectively. Finally, the digital inpainted image is obtained by synthesis. The experimental results show that the digital image inpainting based on multispectral image decomposition synthesis reduces the problem of low image inpainting accuracy caused by the correlation between the color components in the traditional digital image inpainting process, and reduces the mismatch of the inpainting image. Appearance of pseudo color of inpainting image is reduced. MSE of multispectral images inpainting qualities is 2.7951 and PSNR of multispectral images inpainting qualities is 44.1681, so it is superior to traditional image inpainting algorithm. It provides a reliable basis for digital inpainting, digital archives and high fidelity replication of defective Painting Arts.

Robust object removal with an exemplar-based image inpainting approach

Object removal can be accomplished by an image inpainting process which obtains a visually plausible image interpolation of an occluded or damaged region. There are two key components in an exemplar-based image inpainting approach: computing filling priority of patches in the missing region and searching for the best matching patch. In this paper, we present a robust exemplar-based method. In the improved model, a regularized factor is introduced to adjust the patch priority function. A modified sum of squared differences (SSD) and normalized cross correlation (NCC) are combined to search for the best matching patch. We evaluate the proposed method by applying it to real-life photos and testing the removal of large objects. The results demonstrate the effectiveness of the approach.

Image Inpainting Based On Geometric Similarity

This paper proposed a new image inpainting method for restoring a large damaged area in the images called the “geometric image inpainting (GII) method”. The method was developed in response to several common problems encountered in the image inpainting process and provided a high-quality result compared to human vision. In the field of image inpainting, there are three key issues: texture analysis, repairing order and reconstruction of lost image pixels. Though there have been a lot of researches published, few combined all three key issues in their studies. We propose a novel image inpainting method integrating all three key issues at the same time. Experiments showed that the proposed image inpainting method offered better results than other existed methods.