Filters

2020 - 20212
Reset filters

Settings

Has files: true × Author: Gross, Markus × Start date: 2020 ×

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Robust Image Denoising using Kernel Predicting Networks
    (The Eurographics Association, 2021) Cai, Zhilin; Zhang, Yang; Manzi, Marco; Oztireli, Cengiz; Gross, Markus; Aydin, Tunç Ozan; Theisel, Holger and Wimmer, Michael
    We present a new method for designing high quality denoisers that are robust to varying noise characteristics of input images. Instead of taking a conventional blind denoising approach or relying on explicit noise parameter estimation networks as well as invertible camera imaging pipeline models, we propose a two-stage model that first processes an input image with a small set of specialized denoisers, and then passes the resulting intermediate denoised images to a kernel predicting network that estimates per-pixel denoising kernels. We demonstrate that our approach achieves robustness to noise parameters at a level that exceeds comparable blind denoisers, while also coming close to state-of-the-art denoising quality for camera sensor noise.
  • Thumbnail Image
    Item
    Controllable Caustic Animation Using Vector Fields
    (The Eurographics Association, 2020) Rojo, Irene Baeza; Gross, Markus; Günther, Tobias; Wilkie, Alexander and Banterle, Francesco
    In movie production, lighting is commonly used to redirect attention or to set the mood in a scene. The detailed editing of complex lighting phenomena, however, is as tedious as it is important, especially with dynamic lights or when light is a relevant story element. In this paper, we propose a new method to create caustic animations, which are controllable through constraints drawn by the user. Our method blends caustics into a specified target image by treating photons as particles that move in a divergence-free fluid, an irrotational vector field or a linear combination of the two. Once described as a flow, additional user constraints are easily added, e.g., to direct the flow, create boundaries or add synthetic turbulence, which offers new ways to redirect and control light. The corresponding vector field is computed by fitting a stream function and a scalar potential per time step, for which constraints are described in a quadratic energy that we minimize as a linear least squares problem. Finally, photons are placed at their new positions back into the scene and are rendered with progressive photon mapping.