Exemplar-based Image Inpainting Research Articles

An Enhanced Technique for Exemplar based Image Inpainting

Image inpainting is an emerging area of image processing; with the help of this we can fill the misplaced or lost regions of a given image. In real life for example in the museum world the job of inpainting of precious images or paintings is fulfilled with the help of an expert of arts. Like this in the digital world the job of image inpainting is fulfilled with the help of inpainting algorithms. Various algorithms have presented in the past to achieve the task of image inpainting. In this paper, we have suggested a novel exemplar based image inpainting technique. The suggested technique will remove whole object from a given image or a portion of the object & it will offer high quality outcomes.

An efficient forgery detection algorithm for object removal by exemplar-based image inpainting

As a popular image manipulation technique, object removal can be achieved by image-inpainting without any noticeable traces, which poses huge challenges to passive image forensics. The existing detection approach utilizes full search for block matching, resulting in high computational complexity. This paper presents an efficient forgery detection algorithm for object removal by exemplar-based inpainting, which integrates central pixel mapping (CPM), greatest zero-connectivity component labeling (GZCL) and fragment splicing detection (FSD). CPM speeds up suspicious block search by efficiently matching those blocks with similar hash values and then finding the suspicious pairs. To improve the detection precision, GZCL is used to mark the tampered pixels in suspected block pairs. FSD is adopted to distinguish and locate tampered regions from its best-match regions. Experimental results show that the proposed algorithm can reduce up to 90% of the processing time and maintain a detection precision above 85% under different kinds of object-removed images.

An image inpainting method using pLSA-based search space estimation

In this paper, we present a novel exemplar-based image inpainting technique based on the local context measure of the target patch. Three main steps of the proposed method are determination of patch priority, the search space estimation for the candidate patches and the patch completion to fill in the unknown pixels of the target patch. In patch priority, we emphasize on the structure by the spatial relationship of neighborhood similar patches and kernel regression based local image structure. We find the search space, sub-regions of the entire source region similar to the region surrounding the target patch, to find the candidate patches. The said search space is estimated using probabilistic latent semantic analysis (pLSA). Last, we infer the unknown pixels of the target patch using pLSA-based context and histogram similarity measure between the target patch and the candidate patches. Experimental results are found to be good compared to the competitive methods and may be used for digital restoration of images of defective or damaged artifacts.

Hybrid error concealment method combining exemplar-based image inpainting and spatial interpolation

This paper proposes an efficient error concealment method for the reconstruction of pixels that are lost in video communication. The proposed method is developed by combining exemplar-based image inpainting for patch reconstruction and spatial interpolation for pixel reconstruction using adaptive threshold by local complexity. By exemplar-based image inpainting, regions with regular structures are reconstructed. For complex regions with irregular structures, just one pixel is reconstructed using the proposed spatial interpolation method. The proposed spatial interpolation method performs reconstruction by selecting adaptively directional interpolation or neighbor interpolation based on gradient information. Simulation results show that the proposed hybrid method performs reconstruction with significantly improved subjective quality compared with the previous spatial error concealment and image inpainting methods. The proposed method also gives substantial improvements of PSNR compared with the previous methods.

Improved Robust Algorithm for Exemplar based Image Inpainting

Inpainting is technique in which mainly used to filling the region which are damaged and want to recover from unwanted object by collecting the information from the neighbouring pixels. Image inpainting technique has been widely used for reconstructing damaged old photographs and removing unwanted objects from images. In this paper, we present an improved robust algorithm for exemplar based inpainting method by modifying the distance function. The method proved to be effective in removing large objects from an image, ensuring accurate propagation of linear structures, and eliminating the drawback of garbage growing which is a common problem in other methods. Experimental results show that our method improves the quality of image inpainting compared with the conventional exemplar-based image completion algorithms. KeywordsTexture Synthesis, Inpainting, PDE, image gradient etc.

Shadow Removal Using Inpainting and Interpolation Technique

Shadows are viewed as undesired information that strongly affects images. Shadows may cause a high risk to present false color tones, to distort the shape of objects, to merge, or to lose objects. This paper proposes a novel approach for the detection and removal of shadows in an image. Firstly the shadow and non shadow region of the original image is identified by HSV color model. The shadow removal is based on exemplar based image inpainting. Finally, the border between the reconstructed shadow and the non shadow areas undergoes bilinear interpolation to yield a smooth transition between them. They would lead to a better fitting of the shadow and non shadow classes, thus resulting in a potentially better reconstruction quality.

Improved Exemplar based Image Inpainting using Structure Tensor

Image Inpainting is technique in which mainly used to filling the region which are damaged and want to recover from unwanted object by collecting the information from the neighbouring pixels. Image inpainting technique has been widely used for reconstructing damaged old photographs and removing unwanted objects from images. In this paper, we present an improved Exemplar based Structure tensor inpainting method based on the exemplar-based image inpainting technique by modifying the distance function. The method proved to be effective in removing large objects from an image, ensuring accurate propagation of linear structures, and eliminating the drawback of “garbage growing” which is a common problem in other methods. Experimental results show that our method improves the quality of image inpainting compared with the conventional exemplar-based image completion algorithms.

Exemplar based Image Inpainting with Reduced Search Region

Image inpainting is a technique to recover lost data or manually remove data from an image. This process is carried out in such a way that it seems reasonable to human eyes. The lost part of the image is filled with the remaining available data of the image. Inpainting can be done using various techniques, mainly variation based, exemplar based and wavelet based. In this paper, exemplar based image inpainting techniques is used and an algorithm which improves and extends the previously proposed algorithm is presented. Instead of searching the entire image, the proposed approach has reduced the search region by searching the nearby pixels since most of the relevant information lies in neighbouring pixels which reduces time to complete inpainting. This technique is mainly used for removing objects and cracks from the image. General Terms Image processing, Inpainting, Object removal.

Exemplar Based Image Inpainting

Exemplar Based Image Inpainting

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.

Pixel and Patch Reordering for Fast Patch Selection in Exemplar-Based Image Inpainting

This letter presents a scheme to improve the running time of exemplar-based image inpainting, first proposed by Criminisi et al. In the exemplar-based image inpainting, a patch that contains unknown pixels is compared to all the patches in the known region in order to find the best match. This is very time-consuming and hinders the practicality of Criminisi's method to be used in real time. We show that a simple bounding algorithm can significantly reduce number of distance calculations, and thus the running time. Performance of the bounding algorithm is affected by the order of patches that are compared, as well as the order of pixels in a patch. We present pixel and patch ordering schemes that improve the performance of bounding algorithms. Experiments with well-known images used in inpainting literature show that the proposed reordering scheme can reduce running time of the bounding algorithm up to 50%.

Blotches Restoration on Exemplar-based Image Inpainting in Archived

Blotches Restoration on Exemplar-based Image Inpainting in Archived

A Hierarchical Search Space Refinement and Filling for Exemplar based Image Inpainting

There are many real world scenarios where a portion of the image is damaged or lost. Restoring such an image without prior knowledge or a reference image is a difficult task. Image inpainting is a method that focuses on reconstructing the damaged or missing portion of images based on the information available from undamaged areas of the same image. The existing methods fill the missing area from the boundary. Their performance varies while reconstructing structures and textures and many of them restrict the size of the area to be inpainted. In this paper exemplar based inpainting is adopted in a hierarchical framework. A hierarchical search space refinement and hierarchical filling are proposed in this paper which increases the accuracy and handles the extra cost due to multi resolution processing in a better way. The former tries to select an exemplar suitable at all resolution levels restricting the search space from the lower resolution level. The later fills the region at lower resolution level whose results are taken to the higher levels. This makes the non boundary pixels known in the higher resolution level which in turn helps in search space refinement while increasing accuracy.

Research on weighted priority of exemplar-based image inpainting

The priority of the filled patch play a key role in the exemplar-based image inpainting, and it should be determined firstly to optimize the process of image inpainting. A modified image inpainting algorithm is proposed by weighted-priority based on the Criminisi algorithm. The improved algorithm demonstrates better relationship between the data term and the confidence term for the optimization of the priority than the classical Criminisi algorithm. By comparing the effect of the inpainted images with different structure, conclusion can be drawn that the optimal priority should be chosen properly for different images with different structures.

Image in Painting Techniques: A survey

Inpainting is the process of reconstructing lost or deteriorated part of images based on the background information. i. e. image Inpainting fills the missing or damaged region in an image utilizing spatial information of its neighbouring region. Inpainting algorithm have numerous applications. It is helpfully used for restoration of old films and object removal in digital photographs. It is also applied to red-eye correction, super resolution, compression etc. The main goal of the Inpainting algorithm is to modify the damaged region in an image in such a way that the inpainted region is undetectable to the ordinary observers who are not familiar with the original image. There have been several approaches proposed for the image inpainting. This proposed work presents a brief survey of different image inpainting techniques and comparative study of these techniques. In this paper we provide a review of different techniques used for image Inpainting. We discuss different inpainting techniques like image PDE based image inpainting, Exemplar based image inpainting, hybrid inpainting, and texture synthesis based image inpainting and semi-automatic and fast digital Inpainting.

Analysis of a Variational Framework for Exemplar-Based Image Inpainting

In this paper we study some variational models for exemplar-based image inpainting, namely the patch nonlocal means and the patch nonlocal Poisson methods, previously studied by the authors from the experimental point of view. In both cases, the unknowns are the image u to be reconstructed and a weight function w expressing the similarity of patches. As a limit case of the studied framework, the weight function reduces to a correspondence map from the inpainting domain to the known part of the image. We prove the existence and regularity of minima for both functionals. In particular, we prove the existence of optimal correspondence maps which are uniform limits of maps of bounded variation with finitely many values. Then we prove the convergence of an alternating optimization scheme for the variables $(u,w)$. We also prove the convergence in probability of the PatchMatch method, a recently introduced and efficient algorithm for computing optimal correspondence maps. Finally, we display some numerical experiments illustrating the performance and properties of the methods.

FEBEMD and Exemplar based Hybrid Image Inpainting for Occlusion Removal

ABSTRACT In this paper, a hybrid method for occlusion removal using Finite element based Bi-dimensional Empirical Mode Decomposition (FE-BEMD) and Exemplar based image inpainting is discussed. Initially, the image is decomposed into Intrinsic Mode Functions (IMFs) and a Residue using FE-BEMD. Then the Exemplar based image inpainting algorithm is applied to each of the IMFs and residue and the results are added together to get the inpainted image. The results obtained shows that the proposed method works well for removing large objects as well as small damages like scratches from images. General Terms Image decomposition, occlusion removal from images. Keywords Image inpainting, Empirical Mode Decomposition, Intrinsic Mo-de Function, Delaunay Triangles, Texture synthesis, simultaneo-us structure and texture propagation. 1. INTRODUCTION Most of our historical data are in the form of images. Human and environmental factors can affect these images and damage them. The technique of filling-in the missing data in a damaged image in a visually plausible way is called image inpainting. It is also called disocclusion or image completion. Image inpainting is usually used to repair the damaged portions in a picture and also to remove unwanted objects from a picture, like overwriting. Its applications include restoration of old photographs, films and paintings and also cleaning documents. FE-BEMD is a fully data-driven, unsupervised decomposition method that decomposes images into Intrinsic Mode Functions and Residue. When all the IMFs are obtained, the data is actually decomposed into a number of frequency bands, in a nonlinear sense, as this method handles non-stationary signals. The lower IMFs pertain to the edges and higher IMFs pertain to illumination. This paper discusses a method that removes large objects as well as small damages like scratches from images, by applying exemplar based image inpainting to the IMFs and residue obtained by FE-BEMD of the images. The paper is organized as follows. Section 2 describes some of the existing inpainting methods. Section 3 deals with Finite Element based Bi-Dimensional Empirical Mode Decomposition. Section 4 describes exemplar based Image inpainting using FE-BEMD and Section 5 gives the experimental results. The last section provides the conclusion.

A Variational Framework for Exemplar-Based Image Inpainting

Non-local methods for image denoising and inpainting have gained considerable attention in recent years. This is in part due to their superior performance in textured images, a known weakness of purely local methods. Local methods on the other hand have demonstrated to be very appropriate for the recovering of geometric structures such as image edges. The synthesis of both types of methods is a trend in current research. Variational analysis in particular is an appropriate tool for a unified treatment of local and non-local methods. In this work we propose a general variational framework for non-local image inpainting, from which important and representative previous inpainting schemes can be derived, in addition to leading to novel ones. We explicitly study some of these, relating them to previous work and showing results on synthetic and real images.

Improved exemplar-based inpainting algorithm for broken Thangka images

The algorithm of exemplar-based image inpainting was introduced to the digital protection of Thangka image due to the advantage that can repair the broken structure and texture effectively at the same time.This algorithm is good at repairing the specific broken images of Thangka,but is not appropriate to other kinds of broken images.Hence,this paper proposed two aspects of improvement for the deficiencies of exemplar-based broken Thangka image inpainting:the improvement of computing method of confidence and the improvement of computing method of isophote intensity.The problem that optimum exemplar block is not exclusive has been solved.The experimental results show that the improved algorithm can not only get a satisfied inpainting result but also improve repairing efficiency.