8 research outputs found
Um framework para processamento paralelo de algoritmos de aumento de resolução de vĂdeos
Dissertação (mestrado)—Universidade de BrasĂlia, Instituto de CiĂŞncias Exatas, Departamento de CiĂŞncia da Computação, 2013.O aumento dimensional de sinais visuais consiste na alteração do tamanho de uma imagem ou de um vĂdeo para dimensões espaciais maiores, utilizando tĂ©cnicas de processa- mento digital de sinais. Geralmente, esse aumento Ă© feito com a utilização de tĂ©cnicas de interpolação. Contudo, essas tĂ©cnicas de interpolação produzem distorções nas imagens au- mentadas. Tais distorções ocorrem porque a imagem aumentada possui apenas as amostras da imagem original, de dimensões menores, que sĂŁo insu cientes para reconstrução exata do sinal, o que gera efeitos de aliasing. Assim sendo, as tĂ©cnicas de interpolação apenas estimam os coe cientes nĂŁo-amostrados do sinal, o que muitas vezes produz resultados insatisfatĂłrios para muitas aplicações, necessitando de outras tĂ©cnicas para reconstituir os coe cientes nĂŁo-amostrados com maior precisĂŁo. Para melhorar a aproximação de uma imagem estimada com relação Ă imagem origi- nal, existem tĂ©cnicas que reconstroem os coe cientes nĂŁo-amostrados. Essas tĂ©cnicas sĂŁo chamadas de super-resolução. Elas consistem em aumentar a resolução utilizando, geral- mente, informações de outras imagens em baixa ou alta-resolução para estimar a informação faltante na imagem que se deseja ampliar. Super-resolução Ă© um processo computacionalmente intenso, onde a complexidade dos algoritmos sĂŁo, geralmente, de ordem exponencial no tempo em função do bloco ou do fa- tor de ampliação. Portanto, quando essas tĂ©cnicas sĂŁo aplicadas para vĂdeos, Ă© necessário que o algoritmo seja extremamente rápido. O problema Ă© que os algoritmos mais com- putacionalmente e cientes, nem sempre sĂŁo aqueles que produzem os melhores resultados visuais. Sendo assim, este trabalho propõe um framework para melhorar o desempenho de diversos algoritmos de super-resolução atravĂ©s de estratĂ©gias de processamento seletivo e paralelo. Para isso, nesta dissertação sĂŁo examinadas as propriedades dos resultados produzidos pelos algoritmos de super-resolução e os resultados produzidos utilizando-se tĂ©cnicas de interpolação. Com essas propriedades, Ă© encontrado um critĂ©rio para classi car as regiões em que os resultados produzidos sejam visualmente equivalentes, nĂŁo importando o mĂ©todo utilizado para ampliação. Nessas regiões de equivalĂŞncia utiliza-se um algoritmo de interpolação, que Ă© muito mais veloz do que os computacionalmente complexos de super-resolução. Assim, consegue-se reduzir o tempo de processamento sem prejudicar a qualidade visual do vĂdeo ampliado. AlĂ©m dessa abordagem, este trabalho tambĂ©m propõe uma estratĂ©gia de divisĂŁo de dados entre diferentes tarefas para que a operação de aumento de resolução seja realizada de forma paralela. Um resultado interessante do modelo proposto Ă© que ele desacopla a abstração de distribuição de carga da função de aumento dimensional. Em outras palavras, diferentes mĂ©todos de super-resolução podem explorar os recursos do framework sem que para isso seus algoritmos precisem ser modi cados para obtenção do paralelismo. Isso torna o framework portável, escalável e reusável por diferentes mĂ©todos de super-resolução. ______________________________________________________________________________ ABSTRACTThe magni cation of visual signals consists of changing the size of an image or a video to larger spatial dimensions, using digital signal processing techniques. Usually, this mag- ni cation is done using numerical interpolation methods. However, these interpolation methods tend to produce some distortions in the increased images. Such distortions oc- cours because the interpolated image is reconstructed using only the original image samples, which are insu cients for the accurate signal reconstruction, generating aliasing e ects. These interpolation techniques only approximate the non-sampled signal coe cients, pro- ducing unsatisfactory results for many applications. Thus, for these applications, others techniques to estimate the non-sampled coe cients are needed. To improve the estimation accuracy of an image with respect to the original, the super- resolution techniques are used to reconstruct the non-sampled coe cients. Generally, these super-resolution techniques enhance the increased image using information of other images to estimate the missing information. Super-resolution is a computationally intensive process, where the algorithms com- plexity are, generally, exponential in time as function of the block size or magni cation factor. Therefore, when these techniques are applied for videos, it is required that the super-resolution algorithm be extremely fast. However, more computationally e cient algorithms are not always those that produce the best visual results. Therefore, this work proposes a framework to improve the performance of various super- resolution algorithms using selective processing and parallel processing strategies. Thus, this dissertation examines the properties of the results produced by the super-resolution algorithms and the results produced by using interpolation techniques. From these proper- ties, is achieved a criterion to classify regions wherein the results produced are equivalent (using both super-resolution or interpolation). In these regions of equivalence, the in- terpolation algorithms are used to increase the dimensions. In the anothers regions, the super-resolution algorithms are used. As interpolation algorithms are faster than the com- putationally complex super-resolution algorithms, the idea is decrease the processing time without a ecting the visual quality of ampli ed video. Besides this approach, this paper also proposes a strategy to divide the data among various processes to perform the super-resolution operation in parallel. An interesting re- sult of the proposed model is the decoupling of the super-resolution algorithm and the parallel processing strategy. In other words, di erent super-resolution algorithms can ex- plore the features of the proposed framework without algorithmic modi cations to achieve the parallelism. Thus, the framework is portable, scalable and can be reusable by di erent super-resolution methods
Layer Decomposition Learning Based on Gaussian Convolution Model and Residual Deblurring for Inverse Halftoning
Layer decomposition to separate an input image into base and detail layers
has been steadily used for image restoration. Existing residual networks based
on an additive model require residual layers with a small output range for fast
convergence and visual quality improvement. However, in inverse halftoning,
homogenous dot patterns hinder a small output range from the residual layers.
Therefore, a new layer decomposition network based on the Gaussian convolution
model (GCM) and structure-aware deblurring strategy is presented to achieve
residual learning for both the base and detail layers. For the base layer, a
new GCM-based residual subnetwork is presented. The GCM utilizes a statistical
distribution, in which the image difference between a blurred continuous-tone
image and a blurred halftoned image with a Gaussian filter can result in a
narrow output range. Subsequently, the GCM-based residual subnetwork uses a
Gaussian-filtered halftoned image as input and outputs the image difference as
residual, thereby generating the base layer, i.e., the Gaussian-blurred
continuous-tone image. For the detail layer, a new structure-aware residual
deblurring subnetwork (SARDS) is presented. To remove the Gaussian blurring of
the base layer, the SARDS uses the predicted base layer as input and outputs
the deblurred version. To more effectively restore image structures such as
lines and texts, a new image structure map predictor is incorporated into the
deblurring network to induce structure-adaptive learning. This paper provides a
method to realize the residual learning of both the base and detail layers
based on the GCM and SARDS. In addition, it is verified that the proposed
method surpasses state-of-the-art methods based on U-Net, direct deblurring
networks, and progressively residual networks
ID Photograph hashing : a global approach
This thesis addresses the question of the authenticity of identity photographs, part of the documents required in controlled access. Since sophisticated means of reproduction are publicly available, new methods / techniques should prevent tampering and unauthorized reproduction of the photograph. This thesis proposes a hashing method for the authentication of the identity photographs, robust to print-and-scan. This study focuses also on the effects of digitization at hash level. The developed algorithm performs a dimension reduction, based on independent component analysis (ICA). In the learning stage, the subspace projection is obtained by applying ICA and then reduced according to an original entropic selection strategy. In the extraction stage, the coefficients obtained after projecting the identity image on the subspace are quantified and binarized to obtain the hash value. The study reveals the effects of the scanning noise on the hash values of the identity photographs and shows that the proposed method is robust to the print-and-scan attack. The approach focusing on robust hashing of a restricted class of images (identity) differs from classical approaches that address any imageCette thèse traite de la question de l’authenticité des photographies d’identité, partie intégrante des documents nécessaires lors d’un contrôle d’accès. Alors que les moyens de reproduction sophistiqués sont accessibles au grand public, de nouvelles méthodes / techniques doivent empêcher toute falsification / reproduction non autorisée de la photographie d’identité. Cette thèse propose une méthode de hachage pour l’authentification de photographies d’identité, robuste à l’impression-lecture. Ce travail met ainsi l’accent sur les effets de la numérisation au niveau de hachage. L’algorithme mis au point procède à une réduction de dimension, basée sur l’analyse en composantes indépendantes (ICA). Dans la phase d’apprentissage, le sous-espace de projection est obtenu en appliquant l’ICA puis réduit selon une stratégie de sélection entropique originale. Dans l’étape d’extraction, les coefficients obtenus après projection de l’image d’identité sur le sous-espace sont quantifiés et binarisés pour obtenir la valeur de hachage. L’étude révèle les effets du bruit de balayage intervenant lors de la numérisation des photographies d’identité sur les valeurs de hachage et montre que la méthode proposée est robuste à l’attaque d’impression-lecture. L’approche suivie en se focalisant sur le hachage robuste d’une classe restreinte d’images (d’identité) se distingue des approches classiques qui adressent une image quelconqu
Digital watermarking and novel security devices
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
A Machine Learning Approach for Watermarking in Dithered Halftone Images
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