Monte Carlo Noise Reduction Algorithm Based on Deep Neural Network in Efficient Indoor Scene Rendering System
Because of its flexibility and universality, Monte Carlo integral has become the preferred algorithm of most realistic image synthesis. However, the quality of rendered images is often affected by the estimated variance, which is mainly reflected in image noise visually. To reduce the variance, Mont...
Saved in:
Main Authors: | Xiwen Chen, Jianfei Shen |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2022-01-01
|
Series: | Advances in Multimedia |
Online Access: | http://dx.doi.org/10.1155/2022/9169772 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Performance Evaluation and Optimization of 3D Gaussian Splatting in Indoor Scene Generation and Rendering
by: Xinjian Fang, et al.
Published: (2025-01-01) -
Efficient Monte Carlo simulation of streamer discharges with deep-learning denoising models
by: F M Bayo-Muñoz, et al.
Published: (2025-01-01) -
Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition
by: Patcharin Kamsing, et al.
Published: (2020-01-01) -
Monte Carlo simulation of interferometric measurement and wavefront shaping under influence of shot noise and camera noise
by: Chunghyeong Lee, et al.
Published: (2025-01-01) -
Interior design assistant algorithm based on indoor scene analysis
by: Lu Zhang
Published: (2025-12-01)