RIBBONS: Rapid Inpainting Based on Browsing of Neighborhood Statistics

Image inpainting refers to filling missing places in images using neighboring pixels. It also has many applications in different tasks of image processing. Most of these applications enhance the image quality by significant unwanted changes or even elimination of some existing pixels. These changes require considerable computational complexities which in turn results in remarkable processing time. In this paper we propose a fast inpainting algorithm called RIBBONS based on selection of patches around each missing pixel. This would accelerate the execution speed and the capability of online frame inpainting in video. The applied cost-function is a combination of statistical and spatial features in all neighboring pixels. We evaluate some candidate patches using the proposed cost function and minimize it to achieve the final patch. Experimental results show the higher speed of 'Ribbons' in comparison with previous methods while being comparable in terms of PSNR and SSIM for the images in MISC dataset

Assessment of sparse-based inpainting for retinal vessel removal

[EN] Some important eye diseases, like macular degeneration or diabetic retinopathy, can induce changes visible on the retina, for example as lesions. Segmentation of lesions or extraction of textural features from the fundus images are possible steps towards automatic detection of such diseases which could facilitate screening as well as provide support for clinicians. For the task of detecting significant features, retinal blood vessels are considered as being interference on the retinal images. If these blood vessel structures could be suppressed, it might lead to a more accurate segmentation of retinal lesions as well as a better extraction of textural features to be used for pathology detection. This work proposes the use of sparse representations and dictionary learning techniques for retinal vessel inpainting. The performance of the algorithm is tested for greyscale and RGB images from the DRIVE and STARE public databases, employing different neighbourhoods and sparseness factors. Moreover, a comparison with the most common inpainting family, diffusion-based methods, is carried out. For this purpose, two different ways of assessing the quality of the inpainting are presented and used to evaluate the results of the non-artificial inpainting, i.e. where a reference image does not exist. The results suggest that the use of sparse-based inpainting performs very well for retinal blood vessels removal which will be useful for the future detection and classification of eye diseases. (C) 2017 Elsevier B.V. All rights reserved.This work was supported by NILS Science and Sustainability Programme (014-ABEL-IM-2013) and by the Ministerio de Economia y Competitividad of Spain, Project ACRIMA (TIN2013-46751-R). The work of Adrian Colomer has been supported by the Spanish Government under the FPI Grant BES-2014-067889.Colomer, A.; Naranjo Ornedo, V.; Engan, K.; Skretting, K. (2017). Assessment of sparse-based inpainting for retinal vessel removal. Signal Processing: Image Communication. 59:73-82. https://doi.org/10.1016/j.image.2017.03.018S73825

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

Livrable D2.2 of the PERSEE project : Analyse/Synthese de Texture

Livrable D2.2 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D2.2 du projet. Son titre : Analyse/Synthese de Textur

3D exemplar-based image inpainting in electron microscopy

In electron microscopy (EM) a common problem is the non-availability of data, which causes artefacts in reconstructions. In this thesis the goal is to generate artificial data where missing in EM by using exemplar-based inpainting (EBI). We implement an accelerated 3D version tailored to applications in EM, which reduces reconstruction times from days to minutes. We develop intelligent sampling strategies to find optimal data as input for reconstruction methods. Further, we investigate approaches to reduce electron dose and acquisition time. Sparse sampling followed by inpainting is the most promising approach. As common evaluation measures may lead to misinterpretation of results in EM and falsify a subsequent analysis, we propose to use application driven metrics and demonstrate this in a segmentation task. A further application of our technique is the artificial generation of projections in tiltbased EM. EBI is used to generate missing projections, such that the full angular range is covered. Subsequent reconstructions are significantly enhanced in terms of resolution, which facilitates further analysis of samples. In conclusion, EBI proves promising when used as an additional data generation step to tackle the non-availability of data in EM, which is evaluated in selected applications. Enhancing adaptive sampling methods and refining EBI, especially considering the mutual influence, promotes higher throughput in EM using less electron dose while not lessening quality.Ein häufig vorkommendes Problem in der Elektronenmikroskopie (EM) ist die Nichtverfügbarkeit von Daten, was zu Artefakten in Rekonstruktionen führt. In dieser Arbeit ist es das Ziel fehlende Daten in der EM künstlich zu erzeugen, was durch Exemplar-basiertes Inpainting (EBI) realisiert wird. Wir implementieren eine auf EM zugeschnittene beschleunigte 3D Version, welche es ermöglicht, Rekonstruktionszeiten von Tagen auf Minuten zu reduzieren. Wir entwickeln intelligente Abtaststrategien, um optimale Datenpunkte für die Rekonstruktion zu erhalten. Ansätze zur Reduzierung von Elektronendosis und Aufnahmezeit werden untersucht. Unterabtastung gefolgt von Inpainting führt zu den besten Resultaten. Evaluationsmaße zur Beurteilung der Rekonstruktionsqualität helfen in der EM oft nicht und können zu falschen Schlüssen führen, weswegen anwendungsbasierte Metriken die bessere Wahl darstellen. Dies demonstrieren wir anhand eines Beispiels. Die künstliche Erzeugung von Projektionen in der neigungsbasierten Elektronentomographie ist eine weitere Anwendung. EBI wird verwendet um fehlende Projektionen zu generieren. Daraus resultierende Rekonstruktionen weisen eine deutlich erhöhte Auflösung auf. EBI ist ein vielversprechender Ansatz, um nicht verfügbare Daten in der EM zu generieren. Dies wird auf Basis verschiedener Anwendungen gezeigt und evaluiert. Adaptive Aufnahmestrategien und EBI können also zu einem höheren Durchsatz in der EM führen, ohne die Bildqualität merklich zu verschlechtern

IMPLEMENTATION OF PATCH BASED INPAINTING METHOD

ABSTRACTInpainting is an emerging field in image processing research area. Inpainting is the retouching process which is used to remove or change the deteriorated/destroyed parts of an image and make the image real visualization to human eye in a plausible way. Different algorithms are proposed for this image inpainting to get quality in image and also time taking to process the inpainting technique. It is more useful in the areas of digital photography, films too. Many algorithms had proposed for this inpainting method and from those algorithms Exemplar Based Inpainting has got more popularized algorithm. Different approaches had applied to get reduce the time complexities in achieving the realistic appearance of the image. In this we presenting a new approach algorithm which makes the exemplar approach will be little faster than previous algorithms by using patch-based.Keywords: Inpainting, Exemplar based, digital Photography, algorithms, image Processin