An Improved Image Denoising Model Based on Nonlocal Means Filter

Jin, Yan; Jiang, Wenyu; Shao, Jianlong; Lu, Jin

doi:10.1155/2018/8593934

Cited by 11 publications

(3 citation statements)

References 45 publications

(78 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This improved MLS reconstruction method does not require the topological connection information of the model, so it not only can effectively denoise, but also can effectively reconstruct sharp features from the point model. A non-local denoising method of the point model was proposed and this method expressed the local geometric features at each sampling point as "geometric gray value", and uses all possible local similar areas on the surface of the model for self-prediction to achieve the purpose of smoothing and denoising (9).…”

Section: Denoisingmentioning

confidence: 99%

A Development Perspective of Point-Based Computer Graphics

Mukti

2021

Sci Insigt

View full text Add to dashboard Cite

Every object has its characteristic shape, appearance and responses to physical interactions. Computer graphics center on those three components of an object to bring them onto the computer display. With the rapid development of three dimensional (3D) printing technology, the accuracy of the focused object’s geometry was put forward. Point-based graphing is a way to taking the role in rendering the huge 3D sampled data. Based on the digital geometry processing of point-sampled model, various algorithms were reviewed, and some related key techniques were compared with the potential perspective of the future work in this area was also presented．

show abstract

Section: Denoisingmentioning

confidence: 99%

A Development Perspective of Point-Based Computer Graphics

Mukti

2021

Sci Insigt

View full text Add to dashboard Cite

show abstract

“…This is because they improve the reliability of the image processing system in terms of their performance. The processes that make up preprocessing are designed to eliminate difficulties associated with low saturation, uneven aspect ratio, uneven brightness, and various sorts of noises ( Li & Xu, 2019 ; Jin et al, 2018 ). It also helps to overcome problems that may occur due to incorrect camera calibrations and the presence of unwanted objects or artefacts in the image.…”

Section: Introductionmentioning

confidence: 99%

Drought stress detection technique for wheat crop using machine learning

Gupta

Kaur

2023

PeerJ Computer Science

View full text Add to dashboard Cite

The workflow of this research is based on numerous hypotheses involving the usage of pre-processing methods, wheat canopy segmentation methods, and whether the existing models from the past research can be adapted to classify wheat crop water stress. Hence, to construct an automation model for water stress detection, it was found that pre-processing operations known as total variation with L1 data fidelity term (TV-L1) denoising with a Primal-Dual algorithm and min-max contrast stretching are most useful. For wheat canopy segmentation curve fit based K-means algorithm (Cfit-kmeans) was also validated for the most accurate segmentation using intersection over union metric. For automated water stress detection, rapid prototyping of machine learning models revealed that there is a need only to explore nine models. After extensive grid search-based hyper-parameter tuning of machine learning algorithms and 10 K fold cross validation it was found that out of nine different machine algorithms tested, the random forest algorithm has the highest global diagnostic accuracy of 91.164% and is the most suitable for constructing water stress detection models.

show abstract

“…e image can be affected by noise in the process of acquisition and transmission, resulting in a decline in image quality. At present, traditional image restoration methods [1][2][3] such as filtering mainly focus on suppressing and reducing noise, but some image information will be lost while denoising. With the development of nonlinear dynamics, SR phenomena are well demonstrated wherein the response of nonlinear systems can be enhanced with the presence of internal or external noise.…”

Section: Introductionmentioning

confidence: 99%

Image Restoration Based on Stochastic Resonance in a Parallel Array of Fitzhugh–Nagumo Neuron

Zhang

et al. 2020

Complexity

View full text Add to dashboard Cite

The poor denoising effect for noisy grayscale images with traditional processing methods would be obtained under strong noise condition, and some image details would be lost. In this paper, a parallel array model of Fitzhugh–Nagumo (FHN) neurons was proposed, which can restore noisy grayscale images well with low peak signal-to-noise ratio (PSNR) conditions and the image details are better preserved. Firstly, the row-column scanning method was used to convert the 2D grayscale image into a 1D signal, and then the 1D signal was converted into a binary pulse amplitude modulation (BPAM) signal by signal modulation. The modulated signal was input to an FHN parallel array for stochastic resonance (SR). Finally, the array output signal was restored to a 2D gray image, and the image restoration effect was analyzed based on the PSNR and Structural SIMilarity (SSIM) index. It is shown that the SR effect can be exhibited in an array of FHN neuron nonlinearities by increasing the array size, and the image restoration effect is significantly better than the traditional image restoration method, and larger PSNR and SSIM can be obtained. It provides a new idea for grayscale image restoration in a low PSNR environment.

show abstract

An Improved Image Denoising Model Based on Nonlocal Means Filter

Cited by 11 publications

References 45 publications

A Development Perspective of Point-Based Computer Graphics

A Development Perspective of Point-Based Computer Graphics

Drought stress detection technique for wheat crop using machine learning

Image Restoration Based on Stochastic Resonance in a Parallel Array of Fitzhugh–Nagumo Neuron

Contact Info

Product

Resources

About