Comparative Study of OpenCV Inpainting Algorithms

Digital image processing has been a significant and important part in the realm of computing science since its inception. It entails the methods and techniques that are used to manipulate a digital image using a digital computer. It is a type of signal processing in which the input and output maybe image or features/characteristics associated with that image. In this age of advanced technology, digital image processing has its uses manifold, some major fields being image restoration, medical field, computer vision, color processing, pattern recognition and video processing. Image inpainting is one such important domain of image processing. It is a form of image restoration and conservation. This paper presents a comparative study of the various digital inpainting algorithms provided by Open CV (a popular image processing library) and also identifies the most effective inpainting algorithm on the basis of Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and runtime metrics

On surface completion and image inpainting by biharmonic functions: Numerical aspects

Numerical experiments with smooth surface extension and image inpainting using harmonic and biharmonic functions are carried out. The boundary data used for constructing biharmonic functions are the values of the Laplacian and normal derivatives of the functions on the boundary. Finite difference schemes for solving these harmonic functions are discussed in detail.Comment: Revised 21 July, 2017. Revised 12 January, 2018. To appear in International Journal of Mathematics and Mathematical Science

Telea ve Naiver Stokes Algoritmaları Kullanılarak Görüntülerdeki Bozulmaları Düzeltme

Görseller üzerindeki bozulmaları düzeltmek veya görsel üzerindeki istenilmeyen bazı kısımları, görselin orijinal halini bilmeyen kişilerin algılayamayacağı şekilde kaldırmak veya değiştirmek insanların çok uzun zamandır talep ettiği işlemlerdir. Bilgisayarların bu işlemler için kullanılması hem işlemin kalitesini arttırmış hem de işlemi kolaylaştırmıştır, fakat bilgisayar ortamında yapılıyor olsa da görsel üzerindeki işlemler halen manuel olarak yapılmaktadır. Görüntü boyama (Image Inpainting) yöntemi ile bu işlem hem daha hızlı yapılmaya başlanmış hem de işlem otomatikleştirilmiştir. Open CV kütüphanesi için geliştirilen inpaint_telea ve inpaint_ns sınıfları ile görsel üzerinde görüntü boyama işlemi yapılabilmektedir

Search Space Reduction in Exemplar Based Image Inpainting

This paper aims at developing accelerated exemplary inpaint method. The feature set is considered to be the pixels along with their 8-neighbors. A Multi Phase Search Space Reduction framework namely Systematic Reduction of Information System (SRIS) is employed. SRIS, basically is a roughest based approach which imputes the missing values in an adaptive manner. In this approach the order of inpainting pixels is determined by a simple but effective priority term. The best exemplar is determined based on a similarity metric which is derived by element wise difference of informative pixels of inpaint window and the corresponding pixels of the source region window

Deep image prior inpainting of ancient frescoes in the Mediterranean Alpine arc

The unprecedented success of image reconstruction approaches based on deep neural networks has revolutionised both the processing and the analysis paradigms in several applied disciplines. In the field of digital humanities, the task of digital reconstruction of ancient frescoes is particularly challenging due to the scarce amount of available training data caused by ageing, wear, tear and retouching over time. To overcome these difficulties, we consider the Deep Image Prior (DIP) inpainting approach which computes appropriate reconstructions by relying on the progressive updating of an untrained convolutional neural network so as to match the reliable piece of information in the image at hand while promoting regularisation elsewhere. In comparison with state-of-the-art approaches (based on variational/PDEs and patch-based methods), DIP-based inpainting reduces artefacts and better adapts to contextual/non-local information, thus providing a valuable and effective tool for art historians. As a case study, we apply such approach to reconstruct missing image contents in a dataset of highly damaged digital images of medieval paintings located into several chapels in the Mediterranean Alpine Arc and provide a detailed description on how visible and invisible (e.g., infrared) information can be integrated for identifying and reconstructing damaged image regions.Comment: 26 page

New similarity Measure for Exemplar Based in Painting

In this paper we intend to illustrate a utility and application of Kriging approximations in image processing problem designated by inpainting or filling in. We also review three state of the art infilling algorithms that deal with higher order PDE, Total Variation and exemplar-based approach. The computer model, a simple idea, we propose addresses this problem in deterministic way, and thus a response from a model lacks random error, i.e., repeated runs for the same input parameters gives the same response from the model. In its simple sense, Kriginng problem is related to the more general problem of predicting output from a computer model at untried inputs. Hence it lends it self for solving inpainting problem. Experimental results show that the proposed model yields qualitative results that are comparable to the existing complex approaches. The proposed method is very effective and simple to fill small gaps