# Classifying degraded images over various levels of degradation

arXiv: https://arxiv.org/abs/2006.08145 · HTML: https://mlsanity.com/paper/2006.08145/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Network](https://mlsanity.com/paper/2111.12971/) (2021)
- [Fidelity Estimation Improves Noisy-Image Classification With Pretrained Networks](https://mlsanity.com/paper/2106.00673/) (2021)
- [Picking Up Quantization Steps for Compressed Image Classification](https://mlsanity.com/paper/2304.10714/) (2023)
- [Learning degraded image classification with restoration data fidelity](https://mlsanity.com/paper/2101.09606/) (2021)
- [Nonuniform Defocus Removal for Image Classification](https://mlsanity.com/paper/2106.13864/) (2021)

## Abstract (arXiv)

Classification for degraded images having various levels of degradation is very important in practical applications. This paper proposes a convolutional neural network to classify degraded images by using a restoration network and an ensemble learning. The results demonstrate that the proposed network can classify degraded images over various levels of degradation well. This paper also reveals how the image-quality of training data for a classification network affects the classification performance of degraded images.