Next generation X-ray computed tomography, based on photon-counting detectors, is now clinically available. These new detectors come with the promise of higher contrast-to-noise ratio and spatial resolution and improved low-dose imaging. However, the multi-bin nature of photon-counting detectors renders the image reconstruction problem more difficult. Common approaches, such as the two-step projection-based approach, may result in material basis images with an excessive degree of noise, which limits the clinical usefulness of the images. One possible solution is to "assist"the conventional image reconstruction by post-processing the reconstructed images using deep learning. Such networks are often trained using some pixel-wise loss, such as the mean squared error. This low-level per-pixel comparison is known to lead to over-smoothing and a loss of fine-grained details that are important to the perceptual quality and clinical usefulness of the image. In this abstract, we propose to tackle this issue by including an adversarial loss based on the Wasserstein generative adversarial network with gradient penalty. The adversarial loss will encourage the distribution of the processed images to be similar to that of the ground truth. This helps prevent over-smoothing and ensures that the ground truth texture is well preserved. In particular, we train a version of the UNet using a combination of the mean absolute error and an adversarial loss to correct for noise in the material basis images. We demonstrate that the proposed method can produce denoised virtual monoenergetic images, with realistic texture, at a range of energy levels.