A New Regularization Model Based on Non-Local Means for Image Deblurring

Image deblurring with noise is a typical ill-posed problem needs regularization. Various regularization models were proposed during several decades study, such as Tikhonov and TV. A new regularization model based non-local similarity constrains is proposed in this paper, which used l2 non-local norms and could be easily solved by fast non-local image denoising algorithm. By combining with Bregmanrized operator splitting (BOS) algorithm, a fast and efficient iterative three step image deblurring scheme is given. Experimental results show that proposed regularization model obtained better results on ten common test images than other similar regularization model including newly proposed NLTV regularization, both in deblurring performance (PSNR and MSSIM) and processing speed.

Download Full-text

Symmetric Normal Inverse Gaussian Model based Non-local Image Denoising

Journal of Convergence Information Technology ◽

10.4156/jcit.vol7.issue17.50 ◽

2012 ◽

Vol 7 (17) ◽

pp. 424-434

Author(s):

Yuanjiang LI ◽

Yuehua LI

Keyword(s):

Image Denoising ◽

Gaussian Model ◽

Inverse Gaussian ◽

Model Based ◽

Non Local ◽

Normal Inverse Gaussian ◽

Local Image

Download Full-text

Non-local similarity based tensor decomposition for hyperspectral image denoising

2017 IEEE International Conference on Image Processing (ICIP) ◽

10.1109/icip.2017.8296610 ◽

2017 ◽

Cited By ~ 3

Author(s):

Fan Xu ◽

Xiao Bai ◽

Jun Zhou

Keyword(s):

Image Denoising ◽

Hyperspectral Image ◽

Tensor Decomposition ◽

Local Similarity ◽

Non Local

Download Full-text

Automated Image Denoising Model: Contribution Towards Optimized Internal and External Basis

International Journal of Image and Graphics ◽

10.1142/s0219467821500510 ◽

2021 ◽

pp. 2150051

Author(s):

S. Elavaar Kuzhali ◽

D. S. Suresh

Keyword(s):

Image Denoising ◽

Image Patch ◽

The Past ◽

Local Means ◽

Hybrid Optimization Algorithm ◽

Proposed Model ◽

Processing Step ◽

Non Local ◽

Chicken Swarm Optimization ◽

Selection Of

For handling digital images for various applications, image denoising is considered as a fundamental pre-processing step. Diverse image denoising algorithms have been introduced in the past few decades. The main intent of this proposal is to develop an effective image denoising model on the basis of internal and external patches. This model adopts Non-local means (NLM) for performing the denoising, which uses redundant information of the image in pixel or spatial domain to reduce the noise. While performing the image denoising using NLM, “denoising an image patch using the other noisy patches within the noisy image is done for internal denoising and denoising a patch using the external clean natural patches is done for external denoising”. Here, the selection of optimal block from the entire datasets including internal noisy images and external clean natural images is decided by a new hybrid optimization algorithm. The two renowned optimization algorithms Chicken Swarm Optimization (CSO), and Dragon Fly Algorithm (DA) are merged, and the new hybrid algorithm Rooster-based Levy Updated DA (RLU-DA) is adopted. The experimental results in terms of some relevant performance measures show the promising results of the proposed model with remarkable stability and high accuracy.

Download Full-text