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This master's thesis deals with methods of increasing the image resolution. It contens as a description of theoretical principles and description of calculations which are wellknown nowdays and are usually used for increasing image resolution both description of new methods which are used in this area of image procesing. It also contens a method which I suggested myself. There is also a description of methods for an evaluation of image similarity and a comparation of results from methods which are described in this thesis. This thesis includes implementations of selected methods in programming language MATLAB. It was created an application, which realizes some methods of increasing image and evaluate their results relation to the original image using PSNR and SSIM index.

Generative Adversarial Networks have been used to obtain state-of-the-art results for low-level computer vision tasks like single image super-resolution, however, they are notoriously difficult to train due to the instability related to the competing minimax framework. Additionally, traditional ensembling mechanisms cannot be effectively applied with these types of networks due to the resources they require at inference time and the complexity of their architectures. In this thesis an alternative method to create ensembles of individual, more stable and easier to train, models by using interpolations in the parameter space of the models is found to produce better results than those of the initial individual models when evaluated using perceptual metrics as a proxy of human judges. This method can be used as a framework to train GANs with competitive perceptual results in comparison to state-of-the-art alternatives. / Generative Adversarial Networks (GANs) har använts för att uppnå state-of-the- art resultat för grundläggande bildanalys uppgifter, som generering av högupplösta bilder från bilder med låg upplösning, men de är notoriskt svåra att träna på grund av instabiliteten relaterad till det konkurrerande minimax-ramverket. Dessutom kan traditionella mekanismer för att generera ensembler inte tillämpas effektivt med dessa typer av nätverk på grund av de resurser de behöver vid inferenstid och deras arkitekturs komplexitet. I det här projektet har en alternativ metod för att samla enskilda, mer stabila och modeller som är lättare att träna genom interpolation i parameterrymden visat sig ge bättre perceptuella resultat än de ursprungliga enskilda modellerna och denna metod kan användas som ett ramverk för att träna GAN med konkurrenskraftig perceptuell prestanda jämfört med toppmodern teknik.

[pt] A indústria mineral vem presenciando, ao longo das últimas décadas, uma redução da qualidade de minério de ferro extraído e o surgimento de novas demandas ambientais. Esta conjuntura fortalece a busca por produtos provenientes do minério de ferro que atendam aos requisitos da indústria siderúrgica, como é o caso de novos aglomerados de minério de ferro. A Microtomografia de Raios-X (microCT) permite a caracterização da estrutura tridimensional de uma amostra, com resolução micrométrica, de forma não-destrutiva. Entretanto, tal técnica apresenta diversas limitações. Quanto melhor a resolução, maior o tempo de análise e menor o volume de amostra adquirido. Modelos de Super Resolução (SR), baseados em Deep Learning, são uma poderosa ferramenta para aprimorar digitalmente a resolução de imagens tomográficas adquiridas em pior resolução. Este trabalho propõe o desenvolvimento de uma metodologia para treinar três modelos de SR, baseados na arquitetura EDSR, a partir de imagens tomográficas de briquetes de redução direta: Um modelo para aumento de resolução de 16 um para 6 um, outro para aumento de 6 um para 2 um, e o terceiro para aumento de 4 um para 2 um. Esta proposta tem como objetivo mitigar as limitações do microCT, auxiliando o desenvolvimento de novas metodologias de Processamento Digital de Imagens para os aglomerados. A metodologia inclui diferentes propostas para avaliação do desempenho da SR, como comparação de PSNR e segmentação de poros. Os resultados apontam que a SR foi capaz de aprimorar a resolução das imagens tomográficas e mitigar ruídos habituais da tomografia. / [en] The mining industry has been witnessing a reduction of extracted iron ore s quality and the advent of new environmental demands. This situation reinforces a search for iron ore products that meet the requirements of the steel industry, such as new iron ore agglomerates. X-ray microtomography (microCT) allows the characterization of a sample s three-dimensional structure, with micrometer resolution, in a non-destructive analysis. However, this technique presents several limitations. Better resolutions greatly increase analysis time and decrease the acquired sample’s volume. Super-Resolution (SR) models, based on Deep Learning, are a powerful tool to digitally enhance the resolution of tomographic images acquired at lower resolutions. This work proposes the development of a methodology to train three SR models, based on EDSR architecture, using tomographic images of direct reduction briquettes: A model for enhancing the resolution from 16 um to 6 um, another for enhancing from 6 um to 2 um, and the third for enhancing 4 um to 2 um. This proposal aims to mitigate the limitations of microCT, assisting the development and implementation of new Digital Image Processing methodologies for agglomerates. The methodology includes different proposals for SR s performance evaluation, such as PSNR comparison and pore segmentation. The results indicate that SR can improve the resolution of tomographic images and reduce common tomography noise.