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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
computational zoom from raw sensor data
| Date | Stars |
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| 2026-07-24 | 333 |
| 2026-07-25 | 333 |
| 2026-07-28 | 333 |
| 2026-07-30 | 333 |
| 2026-08-06 | 333 |
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# zoom-learn-zoom Code for CVPR 2019 paper: Zoom to Learn, Learn to Zoom <a href="https://people.eecs.berkeley.edu/~cecilia77/project-pages/zoom.html" target="_blank">Project Website</a> | <a href="https://arxiv.org/pdf/1905.05169.pdf" target="_blank">Paper</a> This paper shows that when applying machine learning to digital zoom for photography, it is beneficial to use real, RAW sensor data for training. This code is based on tensorflow (tested on V 1.13.1). It has been tested on Ubuntu 16.04 LTS. ##  ## SR-RAW Dataset #### Use SR-RAW SR-RAW training and testing now available <a href="https://drive.google.com/drive/folders/1FHhcrZjYvFm-zliziQIRVzYjlZUCikai?usp=sharing" target="_blank">here</a>. (If you want to try out without downloading the full train/test dataset, please see the section of [quick inference](#quick-inference)) To download testing dataset (7 GB), run: ```bash bash ./scripts/download.sh 19zlN1fqRRm7E_6i5J3B1OskJocVeuvzG test.zip unzip test.zip rm test.zip ``` We used 35 mm images (mostly named '00006' in the sequences) for test. To download training dataset (58 GB), run: ```bash bash ./scripts/download.sh 1qp6z3F4Ru9srwq1lNZr3pQ4kcVN-AOlM train.zip unzip train.zip rm train.zip ``` Training dataset on Baidu Drive: @llp1996 has kindly uploaded the dataset to <a href="https://pan.baidu.com/s/1pRu7DDnpUYaPdXY8Rlb6UQ" target="_blank">Baidu Drive</a>. The key is：wi02. The original issue opened <a href="https://github.com/ceciliavision/zoom-learn-zoom/issues/8#issue-456526689"> here</a>. #### Try with your own data Our model is trained on raw data in <a href="http://arwviewer.com/" target="_blank">Sony Digital Camera Raw</a>. If you use other types of raw data formats, like [DNG](https://helpx.adobe.com/photoshop/digital-negative.html) used by iPhone (you can use the app [Halide](https://itunes.apple.com/us/app/halide-camera/id885697368?mt=8) to store raw from iPhone), it is necessary to fine tune the model with raw data in that format. ## Quick inference We will download the pre-trained model and example raw data. ```bash git clone https://github.com/ceciliavision/zoom-learn-zoom.git cd zoom-learn-zoom bash ./scripts/download.sh 1iForbFhhWqrq22FA1xIusfUpdi8td4Kq model.zip unzip model.zip bash ./scripts/download.sh 1WVSGaKIJVHwphTKhcr9ajolEnBh3aUkR quick_inference.zip unzip quick_inference.zip rm *.zip python3 inference.py ``` Notes about `config/inference.yaml` - To do inference on a folder, set `mode` to `inference` and set `inference_root` (e.g. `./quick_inference/`) - To do inference on a single image, set `mode` to `inference_single` and set `inference_path` (e.g. `./quick_inference/00134.ARW`) - Set `task_folder` (e.g. `./restore_4x`) - Results are saved in `./[task_folder]/[mode]` ## Training #### CoBi loss The implementation of CoBi loss presented in the paper can be found in the `./CX` directory. It's modified based on the original contextual loss <a href='https://github.com/roimehrez/contextualLoss'>implementation</a>. Refer to `./loss.py` to see how it's used. The full training pipeline is under preparation and will be released somewhere around October. #### Data Pre-processing We provide alignment functions and scripts to account for hand motion when capturing the dataset. This is an *optional* step, as CoBi loss does not require pixel-wise aligned data pairs for training. However, we notice that having a preliminary (imprecise) alignment step leads to faster convergence. In summary, we provide: - `./scripts/run_align.sh` is the script that calls `./main_crop.py` and `./main_align_camera.py`, which first aligns field of view and then accounts for hand motion misalignment among images - `./scripts/run_wb.sh` is the script that calls `./main_wb.py` to compute white balance applied to the processed images in the camera ISP To run these scripts, fill in [TRAIN_PATH] with your local training data path, and [TEST_PATH] with your local test data path. If you us
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:48ec220426d685f5, topic:computer-vision
matched fp:48ec220426d685f5, topic:super-resolution