Texture-based Error Analysis for Image Super-Resolution

Magid, Salma Abdel; Lin, Zudi; Wei, Donglai; Zhang, Yulun; Gu, Jinjin; Pfister, Hanspeter

doi:10.1109/cvpr52688.2022.00216

Cited by 7 publications

(1 citation statement)

References 30 publications

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“…When training a deep network on a large number of images, the expectation is for the network to learn to discern the rich semantics of natural images from noise-contaminated test cases. However, several studies have noted that the semantics and knowledge acquired by low-level vision networks differ significantly from our expectations [29,50,51,53]. We argue that the poor generalization ability of denoising models results from our training method, which leads the model to focus on overfitting the training noise rather than learning image reconstruction.…”

Section: Motivationmentioning

confidence: 51%

Masked Image Training for Generalizable Deep Image Denoising

Chen¹,

Gu²,

Liu³

et al. 2023

Preprint

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When capturing and storing images, devices inevitably introduce noise. Reducing this noise is a critical task called image denoising. Deep learning has become the de facto method for image denoising, especially with the emergence of Transformer-based models that have achieved notable state-of-the-art results on various image tasks. However, deep learning-based methods often suffer from a lack of generalization ability. For example, deep models trained on Gaussian noise may perform poorly when tested on other noise distributions. To address this issue, we present a novel approach to enhance the generalization performance of denoising networks, known as masked training. Our method involves masking random pixels of the input image and reconstructing the missing information during training. We also mask out the features in the self-attention layers to avoid the impact of training-testing inconsistency. Our approach exhibits better generalization ability than other deep learning models and is directly applicable to realworld scenarios. Additionally, our interpretability analysis demonstrates the superiority of our method.

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Section: Motivationmentioning

confidence: 51%