Image denoising with multi-layer perceptrons, part 2: training trade-offs and analysis of their mechanisms

By Harold Christopher Burger, Christian J. Schuler, Stefan Harmeling. 2012

34 citations recorded by the source corpus.

Image denoising can be described as the problem of mapping from a noisy image to a noise-free image. In Burger et al. (2012), we show that multi-layer perceptrons can achieve outstanding image denoising performance for various types of noise (additive white Gaussian noise, mixed Poisson-Gaussian noise, JPEG artifacts, salt-and-pepper noise and noise resembling stripes). In this work we discuss in detail which trade-os have to be considered during the training procedure. We will show how to achieve good results and which pitfalls to avoid. By analysing the activation patterns of the hidden units we are able to make observations regarding the functioning principle of multi-layer perceptrons trained for image denoising.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Media Technology, Optimization & Training

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Citations in the corpus

Non-local Neural Networks (2017, methodology): “Block matching was used with neural networks for image denoising [6, 31].”
A survey on instance segmentation: state of the art (2020, methodology): “This idea was successively developed by Block-matching 3D (BM3D) [102-105].”
Joint Rain Detection and Removal from a Single Image with Contextualized Deep Networks (2020, background): “The complexity of rain removal in each iteration is consequently reduced, enabling better estimation, especially in the case of heavy rain.”