When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach
Authors: Ding Liu, Bihan Wen, Xianming Liu, Zhangyang Wang, Thomas Huang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate their denoising performance over the widely used Kodak dataset2, which consists of 24 color images. Table ?? shows the peak signalto-noise ratio (PSNR) results for CBM3D [Dabov et al., 2007a], TNRD [Chen and Pock, 2017], MCWNNM [Xu et al., 2017], Dn CNN [Zhang et al., 2017a], and our proposed method. Table ?? and Table ?? list the performance of high-level vision tasks, i.e., top-1 and top5 accuracy for classification and mean intersection-over-union (Io U) without conditional random field (CRF) postprocessing for semantic segmentation. |
| Researcher Affiliation | Collaboration | 1 University of Illinois at Urbana-Champaign, USA 2 Facebook Inc. 3 Texas A&M University, USA |
| Pseudocode | No | The paper provides network architectures in diagrams but no pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available online1. 1https://github.com/Ding-Liu/Deep Denoising |
| Open Datasets | Yes | We use the training set as in [Chen et al., 2014]. We use the training set as in [Chen et al., 2014]. We evaluate their denoising performance over the widely used Kodak dataset2, which consists of 24 color images. 2http://r0k.us/graphics/kodak/. we train our model on ILSVRC2012 training set. we train our model on the augmented training set of Pascal VOC 2012 as in [Chen et al., 2014]. |
| Dataset Splits | Yes | evaluate the classification accuracy on ILSVRC2012 validation set. test on its validation set. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) are mentioned. |
| Software Dependencies | No | The paper mentions VGG-based networks and SGD, but no specific software versions for libraries or frameworks are provided. |
| Experiment Setup | Yes | We use SGD with a batch size of 32, and the input patches are 48 48 pixels. The initial learning rate is set as 10 4 and is divided by 10 after every 500,000 iterations. The training is terminated after 1,500,000 iterations. λ is empirically set as 0.25. λ is empirically set as 0.5. |