We lifted 14 functions out of this paper's own repositories and ran 9 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 |
|---|---|---|
| LEEPEIQIN/EDSR | pwc_unofficial | 5 of 5 |
| alterzero/DBPN-Pytorch | pwc_unofficial | 4 of 8 |
| akashpalrecha/superres-deformable | pwc_unofficial | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| calc_psnr | Ran | LEEPEIQIN/EDSR/src/utility.py code served (permissive licence) · get_code("d4592ac83c04a5fd") |
| default_conv | Ran | LEEPEIQIN/EDSR/src/model/common.py code served (permissive licence) · get_code("8b0e794d4d8f9b13") |
| is_image_file | Ran | alterzero/DBPN-Pytorch/dataset.py code served (permissive licence) · get_code("0516d5020cce56da") |
| load_img | Ran | alterzero/DBPN-Pytorch/dataset.py code served (permissive licence) · get_code("27ebc73345cd00a0") |
| make_optimizer | Ran | LEEPEIQIN/EDSR/src/utility.py code served (permissive licence) · get_code("5eed111fb0fb051b") |
| projection_conv | Ran | LEEPEIQIN/EDSR/src/model/ddbpn.py code served (permissive licence) · get_code("3cadb1fed3be1893") |
| quantize | Ran | LEEPEIQIN/EDSR/src/utility.py code served (permissive licence) · get_code("b1c1acd3c89a39f3") |
| rescale_img | Ran | alterzero/DBPN-Pytorch/dataset.py code served (permissive licence) · get_code("25678766a66c3ac4") |
| str2bool | Ran | alterzero/DBPN-Pytorch/eval_gan.py code served (permissive licence) · get_code("25c7475539e39da4") |
| denorm | Not yet run | alterzero/DBPN-Pytorch/utils.py code served (permissive licence) · get_code("754eaaec87e45177") |
| gated_conv | Not yet run | akashpalrecha/superres-deformable/src/model/cgc_edsr.py code served (permissive licence) · get_code("b70e2f411ef9239a") |
| gram_matrix | Not yet run | alterzero/DBPN-Pytorch/utils.py code served (permissive licence) · get_code("4be7bb2418de3fb9") |
| norm | Not yet run | alterzero/DBPN-Pytorch/utils.py code served (permissive licence) · get_code("5178867afe8613af") |
| x8_forward | Not yet run | alterzero/DBPN-Pytorch/eval_gan.py code served (permissive licence) · get_code("007d490d5ea4d153") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The feed-forward architectures of recently proposed deep super-resolution networks learn representations of low-resolution inputs, and the non-linear mapping from those to high-resolution output. However, this approach does not fully address the mutual dependencies of low- and high-resolution images. We propose Deep Back-Projection Networks (DBPN), that exploit iterative up- and down-sampling layers, providing an error feedback mechanism for projection errors at each stage. We construct mutually-connected up- and down-sampling stages each of which represents different types of image degradation and high-resolution components. We show that extending this idea to allow concatenation of features across up- and down-sampling stages (Dense DBPN) allows us to reconstruct further improve super-resolution, yielding superior results and in particular establishing new state of the art results for large scaling factors such as 8x across multiple data sets.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1803.02735")
get_code_for_paper("1803.02735")
have("1803.02735")
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