search

chaiNNer-org / spandrel

Spandrel gives your project support for various PyTorch architectures meant for AI Super-Resolution, restoration, and inpainting. Based on the model support implemented in chaiNNer.
MIT License
161 stars 12 forks source link

Low light enhancement #288

Open Euanggelos opened 4 months ago

Euanggelos commented 4 months ago

Hi Could you include support for some of the models included in this link? https://github.com/caiyuanhao1998/MST-plus-plus specially for higher PSNR? Restormer MPRNet MST-L MST++

RunDevelopment commented 4 months ago

We should already support Restormer and MPRNet.

About MST and MST++: These are meant to reconstruct for full spectrum from an RGB image, AFAIK. So given an RGB image, they will output an approximation of the light frequency spectrum for each pixel. I guess this could be used for Low-light enhancement, but not directly as is.

So I don't know which specific models you want us to support. @Euanggelos, could you please link them more directly?

Disonantemus commented 4 months ago

We should already support Restormer and MPRNet.

I tried both today from spandrel Readme links, using ChaiNNer:

Maybe both doesn't have Low-Light Enhancement models, and are in the Results on NTIRE 2022 HSI Dataset just to compare older models.

From NTIRE 2024 Challenge on Low Light Enhancement, seems that RetinexFormer and MST++ are nice aditions, because they ranked top-2.

From Readme, RetinexFormer is supported, but models from links says unsupported in ChaiNNer. Maybe ChaiNNer (0.24.1) doesn't have an updated spandrel that support this.

In summary, I didn't find any Low-Light models in ChaiNNer that works.

chainner-bin (Arch AUR): 0.24.1-1 (Last Release)
distro: Arch Linux x86_64
kernel: 6.6.22-1-lts
shell: bash 5.2.26
term: tmux
cpu: Intel i7-4790 (8) @ 3.600GHz
gpu: AMD ATI Radeon RX 470/480/570/570X/580/580X/590
Disonantemus commented 3 months ago