Comparative Analysis and Evaluation of Image inpainting Algorithms

Image inpainting refers to the task of filling in the missing or damaged regions of an image in an undetectable manner. There are a large variety of image inpainting algorithms existing in the literature. They can broadly be grouped into two categories such as Partial Differential Equation (PDE) based algorithms and Exemplar based Texture synthesis algorithms. However no recent study has been undertaken for a comparative evaluation of these algorithms. In this paper, we are comparing two different types of image inpainting algorithms. The algorithms analyzed are Marcelo Bertalmio’s PDE based inpainting algorithm and Zhaolin Lu et al’s exemplar based Image inpainting algorithm.Both theoretical analysis and experiments have made to analyze the results of these image inpainting algorithms on the basis of both qualitative and quantitative way. Keywords:Image inpainting, Exemplar based, Texture synthesis, Partial Differential Equation (PDE)

A novel image inpainting framework based on multilevel image pyramids

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

Image Inpainting and Enhancement using Fractional Order Variational Model

The intention of image inpainting is to complete or fill the corrupted or missing zones of an image by considering the knowledge from the source region. A novel fractional order variational image inpainting model in reference to Caputo definition is introduced in this article. First, the fractional differential, and its numerical methods are represented according to Caputo definition. Then, a fractional differential mask is represented in 8-directions. The complex diffusivity function is also defined to preserve the edges. Finally, the missing regions are filled by using variational model with fractional differentials of 8-directions. The simulation results and analysis display that the new model not only inpaints the missing regions, but also heightens the contrast of the image. The inpainted images have better visual quality than other fractional differential filters

Pde based inpainting algorithms: performance evaluation of the Cahn-Hillard model

Image inpainting consists in restoring a missing or a damaged part of an image on the basis of the signal information in the pixels sur- rounding the missing domain. To this aim a suitable image model is needed to represent the signal features to be reproduced within the inpainting domain, also depending on the size of the missing area. With no claim of completeness, in this paper the main streamline of the development of the PDE based models is retraced. Then, the Cahn-Hillard model for binary images is analyzed in detail and its performances are evaluated on some numerical experiments

Pembuatan Aplikasi Objek Removal Dengan Menggunakan Exemplar-Based Inpainting

Teknologi saat ini telah berkembang dengan pesat sehingga memudahkan pengambilan gambar. Namun seringkali terdapat objek yang tidak diinginkan pada gambar yang diambil. Masalah dapat timbul apabila objek tersebut dihilangkan dari gambar, karena akan menghasilkan ruang kosong pada gambar tersebut.Untuk mengatasi masalah tersebut, pengisian ruang kosong (target region) pada gambar dengan menggunakan metode novel based exemplar sebagai metode untuk pengisian gambar. Keunggulan dari metode ini adalah penggunaan urutan pengisian gambar yang dipengaruhi oleh nilai isophote dan jumlah source region.Hasil pengujian menunjukkan bahwa ukuran atau bentuk penyeleksian objek, gradasi, pembiasan warna sangat berpengaruh terhadap hasil inpainting. Prioritas sangat mempengaruhi pengambilan pada source region yang dicari. Gradien yang jelas tanpa dipengaruhi oleh pembiasan, gradasi warna dan blur akan membuat hasil inpainting yang alami

Color Image Inpainting By an Improved Criminisi Algorithm

Due to the incorrect filling order and the fixed size of patch, the traditional examplar-based image inpainting algorithm tends to cause the image structure fracture, texture error extension and so on. So in this paper, it proposes an improved Criminisi algorithm with adaptive adjustment with gradient variation to color image inpainting algorithm. Firstly, to overcome the discontinuity of the edge structure caused by the incorrect filling order, using curvature of isophotes to constraint the filling order. Secondly, in order to solve the lack of the step effect in rich texture region, it adaptively adjusts the sample patch size according to the variation of local gradient. Finally, the local search method is used to find the best matching patch. The experimental results show that the proposed algorithm’s PSNR increased by 1-3dB and obtain better results in terms of different types of images

Smart Image Completion Using Internet Photo Collection

Tohoku University青木 孝

Image de-fencing framework with hybrid inpainting algorithm

Detection and removal of fences from digital images become essential when an important part of the scene turns to be occluded by such unwanted structures. Image de-fencing is challenging because manually marking fence boundaries is tedious and time-consuming. In this paper, a novel image de-fencing algorithm that effectively detects and removes fences with minimal user input is presented. The user is only requested to mark few fence pixels; then, color models are estimated and used to train Bayes classifier to segment the fence and the background. Finally, the fence mask is refined exploiting connected component analysis and morphological operators. To restore the occluded region, a hybrid inpainting algorithm is proposed that integrates exemplar-based technique with a pyramid-based interpolation approach. In contrast to previous solutions which work only for regular pattern fences, the proposed technique is able to remove both regular and irregular fences. A large number of experiments are carried out on a wide variety of images containing different types of fences demonstrating the effectiveness of the proposed approach. The proposed approach is also compared with state-of-the-art image de-fencing and inpainting techniques and showed convincing results. 2016, Springer-Verlag London.Scopu