Algorithms for super-resolution of images based on Sparse Representation and Manifolds

lmage super-resolution is defined as a class of techniques that enhance the spatial resolution of images. Super-resolution methods can be subdivided in single and multi image methods. This thesis focuses on developing algorithms based on mathematical theories for single image super­ resolution problems. lndeed, in arder to estimate an output image, we adopta mixed approach: i.e., we use both a dictionary of patches with sparsity constraints (typical of learning-based methods) and regularization terms (typical of reconstruction-based methods). Although the existing methods already per- form well, they do not take into account the geometry of the data to: regularize the solution, cluster data samples (samples are often clustered using algorithms with the Euclidean distance as a dissimilarity metric), learn dictionaries (they are often learned using PCA or K-SVD). Thus, state-of-the-art methods still suffer from shortcomings. In this work, we proposed three new methods to overcome these deficiencies. First, we developed SE-ASDS (a structure tensor based regularization term) in arder to improve the sharpness of edges. SE-ASDS achieves much better results than many state-of-the- art algorithms. Then, we proposed AGNN and GOC algorithms for determining a local subset of training samples from which a good local model can be computed for recon- structing a given input test sample, where we take into account the underlying geometry of the data. AGNN and GOC methods outperform spectral clustering, soft clustering, and geodesic distance based subset selection in most settings. Next, we proposed aSOB strategy which takes into account the geometry of the data and the dictionary size. The aSOB strategy outperforms both PCA and PGA methods. Finally, we combine all our methods in a unique algorithm, named G2SR. Our proposed G2SR algorithm shows better visual and quantitative results when compared to the results of state-of-the-art methods.

Uma leitura de gênero nos contos "Prelúdio", "Na baía" e "A casa de bonecas", de Katherine Mansfield

This work aims to analyze the short stories “Prelude”, “At the Bay” and “The Doll’s House”, by Katherine Mansfield under the prism of the gender studies (mainly on the works of Joan Scott and Elisabeth Badinter). To reach such objective, and based on the feminist criticism works (especially those of Elaine Showalter and Toril Moi), we analyzed the three stories, which are from the writer’s so-called “family phase”. The present work contains a bibliographical contextualization of Mansfield’s modernist work under three main aspects: modernism, the short story and women’s writing/writings on women. From the analysis of the three short stories, we observed that questions of gender, representation and identity were depicted by means of the preponderance of female characters from all ages, marital statuses and classes. At the end it was possible to verify how Mansfield works contributed to a reflection about places and roles occupied by women in turn of the XIX and XX Centuries, whereas how this author was also in search for her own identity as a woman and as a writer, exactly in a context when women writers and women’s writings started to become more visible face to a predominantly masculine literary canon.