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Benchmarking Denoising Algorithms with Real Photographs
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Benchmarking Denoising Algorithms with Real Photographs

Task and results

In this benchmark we compare some algorithms to denoise the image. We compare the following algorithms: BM3D, KSVD, FOE, WNNM, NCSR, EPLL. We used the RENOIR dataset from Josue Anaya and Adrain Barbu and we measure the algorithm quality with the following metrics: MSE, PSNR, SSIM. Measurements is made on 20 images 512x512 and the results are not realistic. It's necessary to make a correction.

NOTE: The source code was taken from the original articles and adapted to our benchmark.



Mean squared error

Peak signal-to-noise ratio

Structural similarity



[1] T. Ploetz, S. Roth. Benchmarking Denoising Algorithms with Real Photographs. 2017

[2] J. Anaya, A. Barbu. RENOIR - A Dataset for Real Low-Light Image Noise Reduction. 2014

[3] Adrian Barbu's Research. RENOIR - A Dataset of Real Low-Light Images. 2018

[4] T. Ploetz, S. Roth.. The Darmstadt Noise Dataset. 2018

[5] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian. Image denoising by sparse 3D transform- domain collaborative fltering. 2007

[6] D. Zoran, Y. Weiss. From Learning Models of Natural Image Patches to Whole Image Restoration. 2011

[7] S. Gu, L. Zhang, W. Zuo, X. Feng. Weighted Nuclear Norm Minimization with Application to Image Denoising. 2014

[8] M. Aharon, M. Elad, A. Bruckstein. K-SVD: Design of Dictionaries for Sparse Representation. 2005

[9] S. Roth, M. J. Black, Fields of Experts. 2009

[10] W. Donga, L. Zhangb, G. Shia, X. Li. Nonlocally Centralized Sparse Representation for Image Restoration. 2012

[11] H. C. Burger, C. J. Schuler, S. Harmeling. Image denoising: Can plain Neural Networks compete with BM3D?. 2012

[12] Y. Chen, T. Pock. Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration. 2016

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