Here, the authors propose a spatially weighted super‐resolution (SR) algorithm, which takes into consideration the distribution of every information that characterise different image areas. The authors investigate to use a combined spatially weighted regularisation of the bilateral total variation and a second‐order term increasing then the robustness of the proposed SR approach with respect to blur and noise degradations. In addition, the authors propose an iterative Bregman iteration algorithm to resolve the obtained optimisation SR problem. As a result, this regularisation is more efficient and easier to implement; moreover, it preserves well the smooth regions of the image and also sharp edges. Using different simulated and real tests, the authors prove the efficiency of the proposed algorithm compared to some SR methods.