CNN-based Euler's Elastica Inpainting with Deep Energy and Deep Image Prior

Euler's elastica constitute an appealing variational image inpainting model. It minimises an energy that involves the total variation as well as the level line curvature. These components are transparent and make it attractive for shape completion tasks. However, its gradient flow is a singular, anisotropic, and nonlinear PDE of fourth order, which is numerically challenging: It is difficult to find efficient algorithms that offer sharp edges and good rotation invariance. As a remedy, we design the first neural algorithm that simulates inpainting with Euler's Elastica. We use the deep energy concept which employs the variational energy as neural network loss. Furthermore, we pair it with a deep image prior where the network architecture itself acts as a prior. This yields better inpaintings by steering the optimisation trajectory closer to the desired solution. Our results are qualitatively on par with state-of-the-art algorithms on elastica-based shape completion. They combine good rotation invariance with sharp edges. Moreover, we benefit from the high efficiency and effortless parallelisation within a neural framework. Our neural elastica approach only requires 3x3 central difference stencils. It is thus much simpler than other well-performing algorithms for elastica inpainting. Last but not least, it is unsupervised as it requires no ground truth training data.Comment: In Proceedings of the 10th European Workshop on Visual Information Processing, Lisbon, 202

Investigation of Optimal Image Inpainting Techniques for Image Reconstruction and Image Restoration Applications

People in today's society take a lot of pictures with their smartphones and also make an effort to keep their old photographs safe, but with time, those photographs deteriorate. Image inpainting is the art of reconstructing damaged or missing parts of an image. Repairing scratches in photographs or film negatives, or adding or removing elements like stamped dates or "red-eye," are all possible through inpainting. In order to restore the image many techniques have been developed, significant techniques include exemplar based inpainting, coherent based inpainting and method for correction of non-uniform illumination. The four main applications of these image inpainting techniques are scratch removal, text removal, object removal and image restoration. However, all the four image inpainting applications cannot be implemented using a single technique. According to the literature, there has been relatively less work done in the field of image inpainting applications. Investigation has been carried out to find the suitability of these three techniques for the four above mentioned image inpainting applications based on two performance metrics

A tight frame algorithm in image inpainting.

Cheng, Kei Tsi Daniel.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 45-49).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 2 --- Background Knowledge --- p.6Chapter 2.1 --- Image Restoration using Total Variation Norm --- p.6Chapter 2.2 --- An Example of Tight Frame system --- p.10Chapter 2.3 --- Sparse and compressed representation --- p.13Chapter 2.4 --- Existence of minimizer in convex analysis --- p.16Chapter 3 --- Tight Frame Based Minimization --- p.18Chapter 3.1 --- Tight Frames --- p.18Chapter 3.2 --- Minimization Problems and Algorithms --- p.19Chapter 3.3 --- Other Minimization Problems --- p.22Chapter 4 --- Algorithm from minimization problem 3 --- p.24Chapter 5 --- Algorithm from minimization problem 4 --- p.28Chapter 6 --- Convergence of Algorithm 2 --- p.31Chapter 6.1 --- Inner Iteration --- p.31Chapter 6.2 --- Outer Iteration --- p.33Chapter 6.2.1 --- Existence of minimizer --- p.33Chapter 7 --- Numerical Results --- p.37Chapter 8 --- Conclusion --- p.4

Image inpainting by global structure and texture propagation.

Huang, Ting.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (p. 37-41).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Related Area --- p.2Chapter 1.2 --- Previous Work --- p.4Chapter 1.3 --- Proposed Framework --- p.7Chapter 1.4 --- Overview --- p.8Chapter 2 --- Markov Random Fields and Optimization Schemes --- p.9Chapter 2.1 --- MRF Model --- p.10Chapter 2.1.1 --- MAP Understanding --- p.11Chapter 2.2 --- Belief Propagation Optimization Scheme --- p.14Chapter 2.2.1 --- Max-Product BP on MRFs --- p.14Chapter 2.2.2 --- Sum-Product BP on MRFs --- p.15Chapter 3 --- Our Formulation --- p.17Chapter 3.1 --- An MRF Model --- p.18Chapter 3.2 --- Coarse-to-Fine Optimization by BP --- p.21Chapter 3.2.1 --- Coarse-Level Belief Propagation --- p.23Chapter 3.2.2 --- Fine-Level Belief Propagation --- p.24Chapter 3.2.3 --- Performance Enhancement --- p.25Chapter 4 --- Experiments --- p.27Chapter 4.1 --- Comparison --- p.27Chapter 4.2 --- Failure Case --- p.32Chapter 5 --- Conclusion --- p.35Bibliography --- p.3

A numerical study of elastica using constrained optimization methods

Master'sMASTER OF ENGINEERIN

Skin lesion segmentation method for dermoscopic images with convolutional neural networks and semantic segmentation

Melanoma skin cancer is one of the most dangerous forms of skin cancer because it grows fast and causes most of the skin cancer deaths. Hence, early detection is a very important task to treat melanoma. In this article, we propose a skin lesion segmentation method for dermoscopic images based on the U-Net architecture with VGG-16 encoder and the semantic segmentation. Base on the segmented skin lesion, diagnostic imaging systems can evaluate skin lesion features to classify them. The proposed method requires fewer resources for training, and it is suitable for computing systems without powerful GPUs, but the training accuracy is still high enough (above 95 %). In the experiments, we train the model on the ISIC dataset – a common dermoscopic image dataset. To assess the performance of the proposed skin lesion segmentation method, we evaluate the Sorensen-Dice and the Jaccard scores and compare to other deep learning-based skin lesion segmentation methods. Experimental results showed that skin lesion segmentation quality of the proposed method are better than ones of the compared methods.This research was funded by University of Economics Ho Chi Minh City, Vietnam

Computer aided puzzle assembly based on shape and texture information /

Puzzle assembly’s importance lies into application in many areas such as restoration and reconstruction of archeological findings, the repairing of broken objects, solving of the jigsaw type puzzles, molecular docking problem, etc. Puzzle pieces usually include not only geometrical shape information but also visual information of texture, color, continuity of lines, and so on. Moreover, textural information is mainly used to assembly pieces in some cases, such as classic jigsaw puzzles. This research presents a new approach in that pictorial assembly, in contrast to previous curve matching methods, uses texture information as well as geometric shape. The assembly in this study is performed using textural features and geometrical constraints. First, the texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. The feature values are derived by these original and predicted images of pieces. A combination of the feature and confidence values is used to generate an affinity measure of corresponding pieces. Two new algorithms using Fourier based image registration techniques are developed to optimize the affinity. The algorithms for inpainting, affinity and Fourier based assembly are explained with experimental results on real and artificial data. The main contributions of this research are: The development of a performance measure that indicates the level of success of assembly of pieces based on textural features and geometrical shape. Solution of the assembly problem by using of the Fourier based methods

Modelado de sistemas de transmisión y reconstrucción de imágenes basados en mazos de fibra óptica no coherentes

La presente tesis doctoral se enmarca en el ámbito científico de los sistemas de inspección visual . El trabajo se ha centrado en la temática de la supervisión remota de entornos. Este tema resulta de un gran interés en múltiples sectores industriales y de investigación.La finalidad de esta tesis es proponer un nuevo modelo global de calibración y transmisión de imágenes utilizando mazos incoherentes de fibras ópticas. El modelo que se presenta incluye toda la problemática a resolver para calcular la función de transferencia necesaria en la transmisión de imágenes, y también, aspectos generales para diseñar dispositivos de este tipo. Se ha desarrollado toda una metodología de calibración y de formación de imágenes basada en dicho modelo. La misma ha sido validada sobre una instalación experimental que es capaz de caracterizar diferentes mazos incoherentes y, además, evaluar diferentes metodologías de calibración espacial y de formación de imágenes. Para llevar acabo los diferentes experimentos mostrados, se ha desarrollado una aplicación que facilita la evaluación empírica del modelo referenciado ante un amplio abanico de condiciones. Los resultados que se exponen demuestran las ventajas que un sistema de estas características puede aportar a la inspección de entornos remotos con difícil acceso, y/o donde resulta arriesgado el uso de cámaras electrónicas convencionales