Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization
By Raymond H. Chan, Chung-Wa Ho, Mila Nikolova. IEEE Transactions on Image Processing, 2005
1,251 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/2032be0818be583f159cc75f2022ed78222fb772
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Yin Zhang
- Wotao Yin
- Junfeng Yang
- Gabriele Steidl
- Soumik Sarkar
- Radha Poovendran
- Hossein Hosseini
- Adedotun Akintayo
In Computer Vision and Pattern Recognition
- Yin Zhang
- Wotao Yin
- Junfeng Yang
- Gabriele Steidl
- Soumik Sarkar
- Radha Poovendran
- Hossein Hosseini
- Adedotun Akintayo
Citations in the corpus
- Splines in Higher Order TV Regularization (2006, background): “From this point of view, the solution of our minimization problem has a sparse representation in terms of discrete fundamental splines.”
- LLNet: A deep autoencoder approach to natural low-light image enhancement (2015, methodology): “Also, Image denoising tasks have been explored us-ing BM3D (Dabov et al., 2009; 2008; 2007), K-SVD (Elad & Aharon, 2006), and non-linear fil-ters (Chen et al., 1999; Chan et al., 2005).”
- I am Robot: (Deep) Learning to Break Semantic Image CAPTCHAs (2016, methodology): “As previous work has demonstrated effective methods for removing noise from images [24], [25], we consider this a temporary solution, that can increase the cost of the automated attack.”
- A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises (2007, background): “Computation time is also reduced by the factor of 150 to 200 compared with the two-phase algorithm [1].”
- Google's Cloud Vision API is Not Robust to Noise (2017, methodology): “For images of size d1×d2×3, the PSNR is computed as follows [14]:”
- An Efficient TVL1 Algorithm for Deblurring Multichannel Images Corrupted by Impulsive Noise (2009, background): “Recently, a two-phase approach was proposed in [11, 12] for denoising and in [9] for deblurring where impulsive plus Gaussian noise is permitted.”
- Analysis of new top-hat transformation and the application for infrared dim small target detection (2010, background): “Impulsive noise suppression has been an active research area and some specialised methods have been proposed to suppress the impulsive noises [14,15], some of them are very efficient.”