Purpose: Despite theoretical models for achieving laser-based ablation smoothness, methods do not yet exist for assessing the impact of residual roughness after corneal ablation, on retinal polychromatic vision. We developed a method and performed an exploratory study to qualitatively and quantitatively analyze the impact of varying degree of corneal roughness simulated through white and filtered noise, on the retinal image. Methods: A preliminary version of the Indiana Retinal Image Simulator (IRIS) [Jaskulski M., Thibos L., Bradley A., Kollbaum P., et al. (2019) IRIS – Indiana Retinal Image Simulator. https://blogs.iu.edu/corl/iris] was used to simulate the polychromatic retinal image. Using patient-specific Zernike coefficients and pupil diameter, the impact of different levels of chromatic aberrations was calculated. Corneal roughness was modeled via both random and filtered noise [(2013) Biomed. Opt. Express4, 220–229], using distinct pre-calculated higher order Zernike coefficient terms. The outcome measures for the simulation were simulated retinal image, Strehl Ratio and Visual Strehl Ratio computed in frequency domain (VSOTF). The impact of varying degree of roughness (with and without refractive error), spatial frequency of the roughness, and pupil dilation was analyzed on these outcome measures. Standard simulation settings were pupil size = 6 mm, Defocus Z[2, 0] = 2 μm (−1.54D), and Spherical Aberrations Z[4, 0] = 0.15 μm. The signal included the 2–4th Zernike orders, while noise used 7–8th Zernike orders. Noise was scaled to predetermined RMS values. All the terms in 5th and 6th Zernike order were set to 0, to avoid overlapping of signal and noise. Results: In case of a constant roughness term, reducing the pupil size resulted in improved outcome measures and simulated retinal image (Strehl = 0.005 for pupil size = 6 mm to Strehl = 0.06 for pupil size = 3 mm). The calculated image quality metrics deteriorated dramatically with increasing roughness (Strehl = 0. 3 for no noise; Strehl = 0.03 for random noise of 0.25 μm at 6 mm diameter; Strehl = 0.005 for random noise of 0.65 μm at 6 mm diameter). Clear distinction was observed in outcome measures for corneal roughness simulated as random noise compared to filtered noise, further influenced by the spatial frequency of filtered noise. Conclusion: The proposed method enables quantifying the impact of residual roughness in corneal ablation processes at relatively low cost. Since normally laser ablation is an integral process divided on a defined grid, the impact of spatially characterized noise represents a more realistic simulation condition. This method can help comparing different refractive laser platforms in terms of their associated roughness in ablation, indirectly improving the quality of results after Laser vision correction surgery.