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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.

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