An improved non-local means image denoising algorithm

Zhang, Liuyun; Hu, Chao; Wu, Shuang‐Qing; Cui, Jialin; Qiu, Jun

doi:10.1109/icinfa.2017.8079010

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, image noise removal is an important research direction of image processing that has been extensively studied in the past several decades [1]- [8]. Most existing denoising methods are concentrated in additive white Gaussian noise (AWGN) [1]- [16], in which the observed noisy image is modeled as a composition of clean image and AWGN noise: ( ) ( ) ( )  z i x i n i . It is important to note that most of these methods assume that the noise variance of the entire image is fixed so that will inevitably bias the denoising result in the subsequent experiments, which will also have a certain impact on the subsequent application.…”

Section: Introductionmentioning

confidence: 99%

Non-uniform Noise Image Denoising Based on Non-local Means

Wei

et al. 2018

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Restricted by camera hardware, digital images captured by digital cameras are noisy, and the noise content of each color channel of the digital image is not balanced. However, most of the existing denoising algorithms assume that the entire image noise is constant, causing errors in the denoising of color images (non-uniform noise images), affecting image noise removal and texture detail protection. To solve this problem, we propose an evaluation operator that can describe the noise content and texture content in the local area of the image. According to the description value, the image pixels are classified, and heuristic denoising parameters are selected for each class to achieve a balance between noise removal effect and texture retention effect. Experimental results of multiple denoising methods show that the proposed algorithm has better denoising effect on color images.

show abstract

Section: Introductionmentioning

confidence: 99%

Non-uniform Noise Image Denoising Based on Non-local Means

Wei

et al. 2018

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

show abstract

Facial Expression Recognition Based on Gabor Multi-orientation Feature Fusion

Liang

Yang

Luo

2019

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Facial expression is a key to nonverbal communication, which has been confirmed by many different research projects. A change in intensity or magnitude of even one specific facial expression can cause different interpretations. With the continuous and fast development of computer vision and pattern recognition, facial expression recognition has received significant attention recently due to the wide range of commercial and law enforcement application and the availability of feasible technology during 30 years of research. In this thesis, facial expression recognition is studied by applying several commonly used methods in the whole process. By numerical experiment, we find that our approach with Gabor based transformation for face expression feature extraction, combining the advantages of various algorithms, Gabor wavelet transform and non-negative matrix decomposition of facial expressions are used to obtain features, and CNN is used to classify and apply them to facial expression recognition.

show abstract

An improved non-local means image denoising algorithm

Cited by 2 publications

References 6 publications

Non-uniform Noise Image Denoising Based on Non-local Means

Non-uniform Noise Image Denoising Based on Non-local Means

Facial Expression Recognition Based on Gabor Multi-orientation Feature Fusion

Contact Info

Product

Resources

About