Research Article
Single-Image Super-Resolution Using Panchromatic Gradient Prior and Variational Model
Algorithm 1
Algorithm 1A variational approach for single-image SR.
(i) | Input: | | The LR image ; | | Magnification factor. | | Output: | | The SR image . | | Initialization: | | Compute an initial SR image via bicubic | | interpolation; | | Calculate the target gradient via equation (7). | | For: | | For: | | 1. For each extracted image patch , search | | for a set of similar patches across the whole | | image by patch matching. | | 2. Decompose the grouped patches into low-rank | | and sparse components. | | (a) Calculate the sparse matrix via (13); | | (b) Calculate the low-rank matrix via (15). | | 3. Reconstruct the SR image . | | (a) Update the image via (25); | | (b) Update the intermediate variable and via (18) and (22); | | (c) Update the Lagrange multiplier and via (19) and (23); | | (d) = ; | | End; | | End; | | = |
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