Paper Info
Title
Low Light Image Denoising Solution With Contrast Enhancement In Curvelet Domain Using Gaussian Mixture Adaptation Model
Abstract
Images captured under low light are noisy and consist of unidentifiable features. Low light noise problem occurs in imaging devices because of smaller sensor size or insufficient exposure. Low light image denoising is an exacting task in many image processing applications. This paper proposes a patch-based image denoising method for low light images in the curvelet domain with contrast enhancement. Curvelet transform is a directional transform and it gives the best sparse representation for images with edges. Here the Expectation-Maximization (EM) algorithm, based on the Gaussian mixture adaptation method is performed in the curvelet domain for denoising. EM Algorithm helps in computing the Gaussian mixture model (GMM) parameters from the patches which are used in maximum a posteriori estimation to update them. GMM parameters and patches are updated periodically until a satisfactory result is achieved. Simulation is performed on standard test data set, and then extended to natural low light noisy images. The results of the proposed technique are then compared using quality metrics such as Peak Signal to Noise Ratio and Structural Similarity Index. It is observed that the use of curvelet transform in denoising process helps to restore the structural information satisfactorily.
Year
DOI
Venue
2021
10.1142/S021969132050054X
INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING
Keywords
DocType
Volume
Expectation maximization, curvelet transform, Gaussian mixture model, image denoising, maximum a posteriori estimation
Journal
19
Issue
ISSN
Citations 
1
0219-6913
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
K. Sreekala100.34
H. C. Sateesh Kumar200.34
K. B. Raja3349.60