|Figure 1: Results of our denoising method with 64 ray samples per pixel. Our method takes input images (column (b)) and significantly reduces noise level while keeping salient image features (columns (a) and (d)). The key idea is the use of virtual flash images (column (c)), which capture various image features without additional samples. The numbers at the lower right corners the root mean square (RMS) errors computed from the reference solutions.
|We propose an efficient and robust image-space denoising method for noisy images generated by Monte Carlo ray tracing methods. Our method is based on two new concepts: virtual flash images and homogeneous pixels. Inspired by recent developments in flash photography, virtual flash images emulate photographs taken with a flash, to capture various features of rendered images without taking additional samples. Using a virtual flash image as an edge-stopping function, our method can preserve image features that were not captured well only by existing edge-stopping functions such as normals and depth values. While denoising each pixel, we consider only homogeneous pixels -- pixels that are statistically equivalent to each other. This makes it possible to define a stochastic error bound of our method, and this bound goes to zero as the number of ray samples goes to infinity, irrespective of denoising parameters. To highlight the benefits of our method, we apply our method to two Monte Carlo ray tracing methods, photon mapping and path tracing, with various input scenes. We demonstrate that using virtual flash images and homogeneous pixels with a standard denoising method outperforms state-of-the-art image-space denoising methods.
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