Deep Mean-Shift Priors for Image Restoration
Authors: Siavash Arjomand Bigdeli, Matthias Zwicker, Paolo Favaro, Meiguang Jin
NeurIPS 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments and Results Our DAE uses the neural network architecture by Zhang et al. [39]. We generated training samples by adding Gaussian noise to images from Image Net [10]. We experimented with different noise levels and found σ1 = 11 to perform well for all our deblurring and super-resolution experiments. Unless mentioned, for image restoration we always take 300 iterations with step length α = 0.1 and momentum µ = 0.9. |
| Researcher Affiliation | Academia | Siavash A. Bigdeli University of Bern EMAIL Meiguang Jin University of Bern EMAIL Paolo Favaro University of Bern EMAIL Matthias Zwicker University of Bern, and University of Maryland, College Park EMAIL |
| Pseudocode | Yes | Table 1: Gradient descent steps for non-blind (NB), noise-blind (NA), and kernel-blind (KE) image deblurring. |
| Open Source Code | Yes | 1The source code of the proposed method is available at https://github.com/siavashbigdeli/DMSP. |
| Open Datasets | Yes | We generated training samples by adding Gaussian noise to images from Image Net [10].Table 2 reports the average PSNR for 32 images from the Levin et al. [19] and 50 images from the Berkeley [2] segmentation dataset |
| Dataset Splits | No | No explicit details on validation set splits (percentages, counts, or specific pre-defined splits) are provided in the paper. |
| Hardware Specification | Yes | The runtime of our method is linear in the number of pixels, and our implementation takes about 0.2 seconds per iteration for one megapixel on an Nvidia Titan X (Pascal). |
| Software Dependencies | No | The paper mentions 'Our DAE uses the neural network architecture by Zhang et al. [39]' but does not provide specific software dependency names with version numbers (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | Unless mentioned, for image restoration we always take 300 iterations with step length α = 0.1 and momentum µ = 0.9.We used momentum µ = 0.7 and step size α = 0.3 for the unknown image and momentum µk = 0.995 and step size αk = 0.005 for the unknown kernel. |