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In the proposed method, we introduce the following two approaches: (i) simultaneous estimation of the HR frame and the motion blur kernels, and (ii) a new prior probability for correctly representing the HR frame.
Different from conventional methods, the proposed Spatial-Temporal Recurrent Residual Network (STR-ResNet) investigates both spatial and temporal residues, which are represented by the difference between a high resolution (HR) frame and its corresponding low resolution (LR) frame and the difference between adjacent HR frames, respectively.
(d) Reconstructed 90th HR frame using the proposed method.
Step 1: Update of the HR frame x (i).
Adaptive setting of prior probability on HR frame.
where x ( j ) ∈ R N H is a vector form of an original HR frame (j th HR frame) of j th motion-blurred LR frame y (j).
simultaneous estimation of the HR frame and the motion blur kernels, and (ii) a new prior probability for correctly representing the HR frame.
(c) Reconstructed 90th HR frame using the method in [8, 18].
(a) LR frame, (a) HR frame estimated by the proposed method.
(a) LR frame, (b) HR frame estimated by the proposed method.
In the HR frame, the edge regions should have large values of the intensity gradients.
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