Search Results for author: Miika Aittala

Found 12 papers, 9 papers with code

What You Can Learn by Staring at a Blank Wall

no code implementations ICCV 2021 Prafull Sharma, Miika Aittala, Yoav Y. Schechner, Antonio Torralba, Gregory W. Wornell, William T. Freeman, Fredo Durand

We present a passive non-line-of-sight method that infers the number of people or activity of a person from the observation of a blank wall in an unknown room.

Alias-Free Generative Adversarial Networks

6 code implementations23 Jun 2021 Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, Timo Aila

We observe that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner.

Image Generation

Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization

1 code implementation NeurIPS 2019 Miika Aittala, Prafull Sharma, Lukas Murmann, Adam B. Yedidia, Gregory W. Wornell, William T. Freeman, Fredo Durand

We recover a video of the motion taking place in a hidden scene by observing changes in indirect illumination in a nearby uncalibrated visible region.

Analyzing and Improving the Image Quality of StyleGAN

105 code implementations CVPR 2020 Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila

Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.

Image Generation

A Dataset of Multi-Illumination Images in the Wild

no code implementations ICCV 2019 Lukas Murmann, Michael Gharbi, Miika Aittala, Fredo Durand

Collections of images under a single, uncontrolled illumination have enabled the rapid advancement of core computer vision tasks like classification, detection, and segmentation.

Image Relighting

Flexible SVBRDF Capture with a Multi-Image Deep Network

1 code implementation27 Jun 2019 Valentin Deschaintre, Miika Aittala, Fredo Durand, George Drettakis, Adrien Bousseau

Empowered by deep learning, recent methods for material capture can estimate a spatially-varying reflectance from a single photograph.

Graphics I.3

Generating Training Data for Denoising Real RGB Images via Camera Pipeline Simulation

1 code implementation18 Apr 2019 Ronnachai Jaroensri, Camille Biscarrat, Miika Aittala, Frédo Durand

Unfortunately, the commonly used additive white noise (AWGN) models do not accurately reproduce the noise and the degradation encountered on these inputs.

Demosaicking Denoising +1

Single-Image SVBRDF Capture with a Rendering-Aware Deep Network

1 code implementation23 Oct 2018 Valentin Deschaintre, Miika Aittala, Fredo Durand, George Drettakis, Adrien Bousseau

Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in single pictures.

Graphics I.3

Burst Image Deblurring Using Permutation Invariant Convolutional Neural Networks

no code implementations ECCV 2018 Miika Aittala, Fredo Durand

We propose a neural approach for fusing an arbitrary-length burst of photographs suffering from severe camera shake and noise into a sharp and noise-free image.

Deblurring Image Restoration

Differentiable Monte Carlo Ray Tracing through Edge Sampling

1 code implementation SIGGRAPH 2018 Tzu-Mao Li, Miika Aittala, Frédo Durand, Jaakko Lehtinen

We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters.

Noise2Noise: Learning Image Restoration without Clean Data

18 code implementations ICML 2018 Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, Timo Aila

We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption.

Image Restoration Salt-And-Pepper Noise Removal

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