PromptRestorer: A Prompting Image Restoration Method with Degradation Perception
Authors: Cong Wang, Jinshan Pan, Wei Wang, Jiangxin Dong, Mengzhu Wang, Yakun Ju, Junyang Chen
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiment We evaluate Prompt Restorer on benchmarks for 4 image restoration tasks: (a) deraining, (b) deblurring, (c) desnowing, and (d) dehazing. We train separate models for different image restoration tasks. Our Prompt Restorer employs a 3-level encoder-decoder. |
| Researcher Affiliation | Academia | 1The Hong Kong Polytechnic University, 2Nanjing University of Science and Technology, 3Dalian University of Technology, 4Hebei University of Technology, 5Shenzhen University |
| Pseudocode | No | The paper describes the model architecture and equations (e.g., Equation 1, 2, 3, 4, 5, 6, 7, 8) and illustrates components in figures, but it does not include a formal pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any concrete statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | We evaluate Prompt Restorer on benchmarks for 4 image restoration tasks: (a) deraining, (b) deblurring, (c) desnowing, and (d) dehazing. We train separate models for different image restoration tasks. ... We evaluate deblurring results on both synthetic datasets (Go Pro [65], HIDE [76]) and real-world datasets (Real Blur-R [73], Real Blur-J [73]). |
| Dataset Splits | No | The paper refers to standard benchmark datasets (e.g., Go Pro, HIDE, Real Blur) which typically have predefined splits, but it does not explicitly state the training, validation, or test dataset splits (e.g., percentages or sample counts) within the paper. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, or memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the 'Adam W optimizer' but does not specify version numbers for any programming languages, libraries, or other software dependencies. |
| Experiment Setup | Yes | Our Prompt Restorer employs a 3-level encoder-decoder. From level-1 to level-3, the number of CGT is [2, 3, 6], attention heads are [2, 4, 8], and number of channels is [48, 96, 192]. The expanding channel capacity factor β is 4. For downsampling and upsampling, we adopt pixel-unshuffle and pixel-shuffle [77], respectively. We train models with Adam W optimizer with the initial learning rate 3e 4 gradually reduced to 1e 6 with the cosine annealing [63]. The patch size is set as 256 256. |