Recently developed dual domain image denoising (DDID) algorithm and its variants, such as dual domain filter (DDF), achieve remarkable results by combining bilateral filter with frequency-based method. However, this kind of algorithms require large patches to guarantee the denoising performance and most of them produce ringing artifacts due to the Gibbs phenomenon induced by high-contrast details. To address these issues, we propose a Foveated Nonlocal Dual Denoising (FNDD) algorithm by unifying foveated nonlocal means and frequency-based methods. In this way, the ability to preserve the high-contrast details is noticeably improved by exploiting foveated self-similarity (patch similarity) instead of pixel similarity, thus leading to void of artifacts. Moreover, we propose an entropy-based back projection step for compensating the detail loss to further improve the performance. Experimental results validate that FNDD significantly outperforms DDID in terms of both quantitative metrics and subjective visual quality under much smaller patches, and even achieves comparable results against state-of-the-art competitors.