# Learning degraded image classification with restoration data fidelity

arXiv: https://arxiv.org/abs/2101.09606 · HTML: https://mlsanity.com/paper/2101.09606/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 44 references that are in the map.
- First posted on arXiv 23 Jan 2021.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation](https://mlsanity.com/paper/2003.13659/) (2020)
- [Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal](https://mlsanity.com/paper/1503.00593/) (2015)
- [Understanding How Image Quality Affects Deep Neural Networks](https://mlsanity.com/paper/1604.04004/) (2016)
- [Effects of Degradations on Deep Neural Network Architectures](https://mlsanity.com/paper/1807.10108/) (2018)
- [Studying Very Low Resolution Recognition Using Deep Networks](https://mlsanity.com/paper/1601.04153/) (2016)
- [Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning](https://mlsanity.com/paper/1907.03029/) (2019)
- [On The Classification-Distortion-Perception Tradeoff](https://mlsanity.com/paper/1904.08816/) (2019)
- [Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks](https://mlsanity.com/paper/2003.07119/) (2020)
- [Classification Driven Dynamic Image Enhancement](https://mlsanity.com/paper/1710.07558/) (2017)
- [BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration](https://mlsanity.com/paper/2011.01406/) (2020)
- [Fine-to-coarse Knowledge Transfer For Low-Res Image Classification](https://mlsanity.com/paper/1605.06695/) (2016)
- [Classifying degraded images over various levels of degradation](https://mlsanity.com/paper/2006.08145/) (2020)
- [Convolutional Neural Networks Considering Local and Global features for Image Enhancement](https://mlsanity.com/paper/1905.02899/) (2019)

## Built on by (most cited first, 1 of 1)

- [Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting](https://mlsanity.com/paper/2102.12095/) (2021)

## Abstract (arXiv)

Learning-based methods especially with convolutional neural networks (CNN) are continuously showing superior performance in computer vision applications, ranging from image classification to restoration. For image classification, most existing works focus on very clean images such as images in Caltech-256 and ImageNet datasets. However, in most realistic scenarios, the acquired images may suffer from degradation. One important and interesting problem is to combine image classification and restoration tasks to improve the performance of CNN-based classification networks on degraded images. In this report, we explore the influence of degradation types and levels on four widely-used classification networks, and the use of a restoration network to eliminate the degradation's influence. We also propose a novel method leveraging a fidelity map to calibrate the image features obtained by pre-trained classification networks. We empirically demonstrate that our proposed method consistently outperforms the pre-trained networks under all degradation levels and types with additive white Gaussian noise (AWGN), and it even outperforms the re-trained networks for degraded images under low degradation levels. We also show that the proposed method is a model-agnostic approach that benefits different classification networks. Our results reveal that the proposed method is a promising solution to mitigate the effect caused by image degradation.