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Image super-resolution by TV-regularization and Bregman iteration. (English) Zbl 1203.94014
Summary: We formulate a new time dependent convolutional model for super-resolution based on a constrained variational model that uses the total variation of the signal as a regularizing functional. We propose an iterative refinement procedure based on Bregman iteration to improve spatial resolution. The model uses a dataset of low resolution images and incorporates a downsampling operator to relate the high resolution scale to the low resolution one. We present an algorithm for the model and we perform a series of numerical experiments to show evidence of the good behavior of the numerical scheme and quality of the results.

94A08Image processing (compression, reconstruction, etc.)
94A20Sampling theory
44A35Convolution (integral transforms)
65D18Computer graphics, image analysis, and computational geometry
65R10Integral transforms (numerical methods)
Full Text: DOI
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