SNIPS: Solving Noisy Inverse Problems Stochastically
Authors: Bahjat Kawar, Gregory Vaksman, Michael Elad
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the abilities of the proposed paradigm for image deblurring, super-resolution, and compressive sensing. We demonstrate SNIPS on image deblurring, single image super resolution, and compressive sensing, all of which contain non-negligible noise, and emphasize the high perceptual quality of the results, their diversity, and their relation to the MMSE estimate. |
| Researcher Affiliation | Academia | Bahjat Kawar, Gregory Vaksman, Michael Elad Computer Science Department, Technion, Haifa, Israel {bahjat.kawar, grishav, elad}@cs.technion.ac.il |
| Pseudocode | Yes | Algorithm 1: SNIPS |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | In our experiments we use the NCSNv2 [45] network in order to estimate the score function of the prior distribution. Three different NCSNv2 models are used, each trained separately on the training sets of: (i) images of size 64x64 pixels from the Celeb A dataset [27]; (ii) images of size 128x128 pixels from LSUN [56] bedrooms dataset; and (iii) LSUN 128x128 images of towers. |
| Dataset Splits | No | The paper mentions "training sets" and "test sets" but does not specify validation splits or detailed percentages for any data splits (training, validation, or test). |
| Hardware Specification | No | The paper mentions using a network for experiments but does not provide any specific details about the hardware used (e.g., GPU models, CPU types, memory). |
| Software Dependencies | No | The paper mentions using "NCSNv2 [45] network" but does not list any specific software dependencies with version numbers (e.g., Python, PyTorch, specific libraries with versions). |
| Experiment Setup | Yes | For image deblurring, we use a uniform 5x5 blur kernel, and an additive white Gaussian noise with σ0 = 0.1 (referring to pixel values in the range [0, 1]). For super resolution, the images are downscaled using a block averaging filter, i.e., each non-overlapping block of pixels in the original image is averaged into one pixel in the low-resolution image. We use blocks of size 2x2 or 4x4 pixels, and assume the low-resolution image to include an additive white Gaussian noise. For compressive sensing, we use three random projection matrices with singular values of 1, that compress the image by 25%, 12.5%, and 6.25%. |