Abstract
Film grain refers to the specific texture of film-acquired images, due to the physical nature of photographic film. Being a visual signature of such images, there is a strong interest in the film-industry for the rendering of these textures for digital images. Some previous works are able to closely mimic the physics of films and produce high quality results, but are computationally expensive. We propose a method based on a lightweight neural network and a texture aware loss function, achieving realistic results with very low complexity, even for large grains and high resolutions. We evaluate our algorithm both quantitatively and qualitatively with respect to previous work.
| Original language | English |
|---|---|
| Article number | e70076 |
| Journal | Computer Graphics Forum |
| Volume | 44 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 May 2025 |
Keywords
- CCS Concepts
- Computational photography
- Image processing
- Neural networks
- • Computing methodologies → Texturing
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