Conventional learning-based superresolution algorithms tend to boost noise components existing in input images because the algorithms are usually learned in a noise-free environment. Even though a specific noise reduction algorithm is applied to noisy images prior to superresolution, visual quality degradation is inevitable due to the mismatch between noise-free images and denoised images. Accordingly, we present a noise-robust superresolution algorithm that overcomes this problem. In the learning phase, a dictionary classified according to noise level is constructed, and then a high-resolution image is synthesized using the dictionary in the inference phase. Experimental results show that the proposed algorithm outperforms existing algorithms for various noisy images.