Recently, a series of advances were made for image restoration tasks such as image denoising and single image super-resolution. It is particularly remarkable that methods employing different formulations and assumptions achieve comparable top performances. Moreover, the top methods operate at their best on some particular image contents and poorer on other. No method is the best on all the image contents. The methods are complementary in both formulation and performance. We propose a locally adaptive fusion of results of such methods towards an improved restoration result. We work patch-wise and partition the patch space such that per each partition to train anchored regressors from the fused methodsâ output patches to the fusion target result. At test our anchored fusion method applies efficiently the anchored regressors corresponding to the input patches to be fused. Whilst having a low time complexity, we achieve significant improvements over the fused state-of-the-art methods on standard test images for both image denoising and super-resolution tasks (e.g. 0.1 - 0.5dB PSNR).