Brief review of image denoising techniques
L Fan, F Zhang, H Fan, C Zhang - Visual Computing for Industry …, 2019 - Springer
With the explosion in the number of digital images taken every day, the demand for more
accurate and visually pleasing images is increasing. However, the images captured by …
accurate and visually pleasing images is increasing. However, the images captured by …
A review paper: noise models in digital image processing
Noise is always presents in digital images during image acquisition, coding, transmission,
and processing steps. Noise is very difficult to remove it from the digital images without the …
and processing steps. Noise is very difficult to remove it from the digital images without the …
A high-quality denoising dataset for smartphone cameras
A Abdelhamed, S Lin… - Proceedings of the IEEE …, 2018 - openaccess.thecvf.com
The last decade has seen an astronomical shift from imaging with DSLR and point-and-
shoot cameras to imaging with smartphone cameras. Due to the small aperture and sensor …
shoot cameras to imaging with smartphone cameras. Due to the small aperture and sensor …
Image denoising review: From classical to state-of-the-art approaches
At the crossing of the statistical and functional analysis, there exists a relentless quest for an
efficient image denoising algorithm. In terms of greyscale imaging, a plethora of denoising …
efficient image denoising algorithm. In terms of greyscale imaging, a plethora of denoising …
Group-based sparse representation for image restoration
Traditional patch-based sparse representation modeling of natural images usually suffer
from two problems. First, it has to solve a large-scale optimization problem with high …
from two problems. First, it has to solve a large-scale optimization problem with high …
Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization
As a powerful statistical image modeling technique, sparse representation has been
successfully used in various image restoration applications. The success of sparse …
successfully used in various image restoration applications. The success of sparse …
Diffusion weighted image denoising using overcomplete local PCA
Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to
the presence of noise from the measurement process that complicates and biases the …
the presence of noise from the measurement process that complicates and biases the …
From heuristic optimization to dictionary learning: A review and comprehensive comparison of image denoising algorithms
Image denoising is a well explored topic in the field of image processing. In the past several
decades, the progress made in image denoising has benefited from the improved modeling …
decades, the progress made in image denoising has benefited from the improved modeling …
Image restoration via simultaneous nonlocal self-similarity priors
Through exploiting the image nonlocal self-similarity (NSS) prior by clustering similar
patches to construct patch groups, recent studies have revealed that structural sparse …
patches to construct patch groups, recent studies have revealed that structural sparse …
An efficient SVD-based method for image denoising
Q Guo, C Zhang, Y Zhang, H Liu - IEEE transactions on Circuits …, 2015 - ieeexplore.ieee.org
Nonlocal self-similarity of images has attracted considerable interest in the field of image
processing and has led to several state-of-the-art image denoising algorithms, such as block …
processing and has led to several state-of-the-art image denoising algorithms, such as block …