自编码
降噪
人工智能
视频去噪
模式识别(心理学)
计算机科学
图像去噪
样品(材料)
奇异值分解
噪音(视频)
深度学习
人工神经网络
算法
色谱法
化学
多视点视频编码
视频跟踪
对象(语法)
出处
期刊:IEEE Transactions on Neural Networks
[Institute of Electrical and Electronics Engineers]
日期:2019-01-01
卷期号:30 (1): 312-317
被引量:40
标识
DOI:10.1109/tnnls.2018.2838679
摘要
The term ``blind denoising'' refers to the fact that the basis used for denoising is learned from the noisy sample itself during denoising. Dictionary learning- and transform learning-based formulations for blind denoising are well known. But there has been no autoencoder-based solution for the said blind denoising approach. So far, autoencoder-based denoising formulations have learned the model on a separate training data and have used the learned model to denoise test samples. Such a methodology fails when the test image (to denoise) is not of the same kind as the models learned with. This will be the first work, where we learn the autoencoder from the noisy sample while denoising. Experimental results show that our proposed method performs better than dictionary learning (K-singular value decomposition), transform learning, sparse stacked denoising autoencoder, and the gold standard BM3D algorithm.
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