lbasek / Image Denoising Benchmark
Programming Languages
Projects that are alternatives of or similar to Image Denoising Benchmark
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.
Contributors:
- Luka Bašek - https://github.com/lbasek
- Ivan Gradečak - https://github.com/igradeca
Diagrams
Mean squared error
Peak signal-to-noise ratio
Structural similarity
Time
Literature
[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. http://adrianbarburesearch.blogspot.pt/p/renoir-dataset.html. 2018
[4] T. Ploetz, S. Roth.. The Darmstadt Noise Dataset. https://noise.visinf.tu-darmstadt.de/. 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