Continuous Super-Resolution for Recovery of 1-D Image Features: Algorithm and Performance Modeling

Frédéric Champagnat, Guy Le Besnerais, Caroline Kulcsar

Abstract: We consider the recovery of 1-D image features. Such features can be described by a noisy, blurred and undersampled image of a unique 1-D profile. The profile’s recovery is modeled as a 1-D continuous super-resolution (SR) problem. We adopt a functional estimation within a Tikhonov regularization framework. A linear closed-form solution is derived and applied to real data for bar code recovery from low-resolution video frames. Performance modeling in then considered. Thanks to a continuous stochastic model of the input profile, we define a quantitative performance measure which is a mean-square error averaged over a class of profiles with tunable regularity. As a result, an expected SR resolution enhancement ratio is computed, which depends on experimental parameters: SNR, number of input images, sampling rate. A good agreement is found between this theoretical study and empirical performance in experimental SR recovery of bar code profiles.