10,310 research outputs found
Exploiting Image Local And Nonlocal Consistency For Mixed Gaussian-Impulse Noise Removal
Most existing image denoising algorithms can only deal with a single type of
noise, which violates the fact that the noisy observed images in practice are
often suffered from more than one type of noise during the process of
acquisition and transmission. In this paper, we propose a new variational
algorithm for mixed Gaussian-impulse noise removal by exploiting image local
consistency and nonlocal consistency simultaneously. Specifically, the local
consistency is measured by a hyper-Laplace prior, enforcing the local
smoothness of images, while the nonlocal consistency is measured by
three-dimensional sparsity of similar blocks, enforcing the nonlocal
self-similarity of natural images. Moreover, a Split-Bregman based technique is
developed to solve the above optimization problem efficiently. Extensive
experiments for mixed Gaussian plus impulse noise show that significant
performance improvements over the current state-of-the-art schemes have been
achieved, which substantiates the effectiveness of the proposed algorithm.Comment: 6 pages, 4 figures, 3 tables, to be published at IEEE Int. Conf. on
Multimedia & Expo (ICME) 201
Dynamic Batch Norm Statistics Update for Natural Robustness
DNNs trained on natural clean samples have been shown to perform poorly on
corrupted samples, such as noisy or blurry images. Various data augmentation
methods have been recently proposed to improve DNN's robustness against common
corruptions. Despite their success, they require computationally expensive
training and cannot be applied to off-the-shelf trained models. Recently, it
has been shown that updating BatchNorm (BN) statistics of an off-the-shelf
model on a single corruption improves its accuracy on that corruption
significantly. However, adopting the idea at inference time when the type of
corruption is unknown and changing decreases the effectiveness of this method.
In this paper, we harness the Fourier domain to detect the corruption type, a
challenging task in the image domain. We propose a unified framework consisting
of a corruption-detection model and BN statistics update that improves the
corruption accuracy of any off-the-shelf trained model. We benchmark our
framework on different models and datasets. Our results demonstrate about 8%
and 4% accuracy improvement on CIFAR10-C and ImageNet-C, respectively.
Furthermore, our framework can further improve the accuracy of state-of-the-art
robust models, such as AugMix and DeepAug
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