We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| m-tassano/fastdvdnet | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| denoise_seq_fastdvdnet | Ran | m-tassano/fastdvdnet/fastdvdnet.py code served (permissive licence) · get_code("4d5ee3b60d4eaeaa") |
| temp_denoise | Ran | m-tassano/fastdvdnet/fastdvdnet.py code served (permissive licence) · get_code("a2c0730cb5cd2456") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture. Until recently, video denoising with neural networks had been a largely under explored domain, and existing methods could not compete with the performance of the best patch-based methods. The approach we introduce in this paper, called FastDVDnet, shows similar or better performance than other state-of-the-art competitors with significantly lower computing times. In contrast to other existing neural network denoisers, our algorithm exhibits several desirable properties such as fast runtimes, and the ability to handle a wide range of noise levels with a single network model. The characteristics of its architecture make it possible to avoid using a costly motion compensation stage while achieving excellent performance. The combination between its denoising performance and lower computational load makes this algorithm attractive for practical denoising applications. We compare our method with different state-of-art algorithms, both visually and with respect to objective quality metrics.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1907.01361")
get_code_for_paper("1907.01361")
have("1907.01361")
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