PhoCoLens: Photorealistic and Consistent Reconstruction in Lensless Imaging
Authors: Xin Cai, Zhiyuan You, Hailong Zhang, Jinwei Gu, Wentao Liu, Tianfan Xue
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare our approach with other methods and conduct a comprehensive ablation study to evaluate the effectiveness of our design. ... We assess the performance of our proposed method by comparing it against both traditional and learning-based approaches, using qualitative and quantitative measures on the two datasets. |
| Researcher Affiliation | Collaboration | Xin Cai1,2, Zhiyuan You1, Hailong Zhang3, Wentao Liu2,4, Jinwei Gu1, Tianfan Xue1,2 1The Chinese University of Hong Kong, 2Shanghai Artificial Intelligence Laboratory, 3Tsinghua University, 4Sense Time |
| Pseudocode | No | The paper describes the system and methods in text and diagrams (e.g., Fig. 5) but does not include formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Project website: phocolens.github.io. ... Code and data will be available on the project website. |
| Open Datasets | Yes | The Phlat Cam dataset [17] contains 10,000 images across 1,000 classes resized to 384 384 pixels. ... The Diffuser Cam dataset [27] contains 25,000 paired images captured simultaneously using a standard lensed camera (ground truth) and a mask-based lensless camera Diffuser Cam [1]. |
| Dataset Splits | No | For the Phlat Cam dataset: 'We use 990 classes for training and 10 classes for testing, following the original protocol.' For the Diffuser Cam dataset: 'These pairs are split into 24,000 images for training and 1,000 for testing.' No explicit mention of a validation dataset split. |
| Hardware Specification | Yes | Evrey experiment can be completed within two days using a server equipped with eight NVIDIA RTX 3090 GPUs, a 64-core CPU, 256GB of RAM, and 1TB of storage. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'Stable SR' but does not provide specific version numbers for these or other libraries/frameworks. |
| Experiment Setup | Yes | We train the network for 100 epochs with a batch size of 5, using the Adam optimizer [18]. The learning rate is set to 3e-5 for U-Net training in both datasets. For deconvolution kernel training, the learning rate is set at 4e-9 for Phlat Cam and 3e-5 for Diffuser Cam. We set the MSE and LPIPS loss weights to be 1 and 0.05, respectively. |