Image denoising is an essential and important pre-processing step in digital imaging systems. However, most of existing methods are not adaptive in real-world applications due to the complexity of real noise. To address this problem, a novel pyramidal generative structural network (PGSN) is proposed for robust and efficient real-world noisy image denoising. Specifically, we consider the denoising problem as a process of image generation. The procedure is to first build a Gaussian pyramid where a cascade of encoder-decoder networks are used to adaptively capture multi-scale image features and progressively reconstruct the corresponding noise-free image from coarse to fine granularity. Then, we train a conditional form of GAN at each pyramid level. By integrating the conditional GAN approach into the Gaussian pyramid, the proposed network can well combine the image features from different pyramid levels, and an incremental distinction between the real noise and image details is dynamically built up, hence greatly boosting the denoising performance. Extensive experimental results demonstrate that our PGSN gives satisfactory denoising results, and achieves superior performance against the state-of-the-arts.