Deep Mean-Shift Priors for Image Restoration
Authors: Siavash Arjomand Bigdeli, Matthias Zwicker, Paolo Favaro, Meiguang Jin
NeurIPS 2017 | Conference 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 bigdeli@inf.unibe.ch Meiguang Jin University of Bern jin@inf.unibe.ch Paolo Favaro University of Bern favaro@inf.unibe.ch Matthias Zwicker University of Bern, and University of Maryland, College Park zwicker@cs.umd.edu |
| 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. |