TY - GEN
T1 - Deep Convolutional Autoencoder for Estimation of Nonstationary Noise in Images
AU - Ghanbaralizadeh Bahnemiri, Sheyda
AU - Ponomarenko, Mykola
AU - Egiazarian, Karen
PY - 2019/10
Y1 - 2019/10
N2 - A precise estimation of noise parameters is very important in many image processing applications, such as denoising, deblurring, compression, etc. This problem is well studied for the case of stationary noise in images, and much less studied for the case of nonstationary noise. In this paper, we develop an efficient method of nonstationary noise variance estimation in image regions, based on specially designed deep convolutional autoencoder (DCAE) with a small dimensionality reduction. Training of the proposed DCAE is carried out for a large set of image blocks, including fragments of noise free textures, faces and texts. In the numerical analysis, we compare the proposed method and method of blind estimation of nonstationary noise, based on block matching (BM). Additionally, we analyze efficiency of the proposed DCAE in comparison with the conventional autoencoder (AE). We show that usage of the proposed DCAE provides an error of noise variance estimation about 2 times smaller, that the error when the standard AE is used, and 4 times smaller than the variance estimation error of the BM method.
AB - A precise estimation of noise parameters is very important in many image processing applications, such as denoising, deblurring, compression, etc. This problem is well studied for the case of stationary noise in images, and much less studied for the case of nonstationary noise. In this paper, we develop an efficient method of nonstationary noise variance estimation in image regions, based on specially designed deep convolutional autoencoder (DCAE) with a small dimensionality reduction. Training of the proposed DCAE is carried out for a large set of image blocks, including fragments of noise free textures, faces and texts. In the numerical analysis, we compare the proposed method and method of blind estimation of nonstationary noise, based on block matching (BM). Additionally, we analyze efficiency of the proposed DCAE in comparison with the conventional autoencoder (AE). We show that usage of the proposed DCAE provides an error of noise variance estimation about 2 times smaller, that the error when the standard AE is used, and 4 times smaller than the variance estimation error of the BM method.
KW - noise parameters estimation
KW - autoencoder
KW - deep convolutional networks
KW - image denoising
KW - image compression
KW - image visual quality assessment
U2 - 10.1109/EUVIP47703.2019.8946273
DO - 10.1109/EUVIP47703.2019.8946273
M3 - Conference contribution
SN - 978-1-7281-4497-9
T3 - European Workshop on Visual Information Processing
SP - 238
EP - 243
BT - 2019 8th European Workshop on Visual Information Processing (EUVIP)
PB - IEEE
T2 - European Workshop on Visual Information Processing
Y2 - 1 January 1900
ER -