Open Access
Neural Network Based Noise Identification in Digital Images
TLDR
A new methodology based on neural network for identifying the different types of noise such as Non Gaussian, Gaussian white, Salt and Pepper and Speckle noise is proposed.Abstract:
Image noise is unwanted information in an image and can occur at any moment of time such as during image capture, transmission, or processing and it may or may not depend on image content. In order to remove the noise from the noisy image, prior knowledge about the nature of noise must be known otherwise noise removal causes the image blurring. Identifying nature of noise is a challenging problem. Many researchers have proposed their ideas on image noise identification and each of the work has its assumptions, advantages and limitations. In this paper, we proposed a new methodology based on neural network for identifying the different types of noise such as Non Gaussian, Gaussian white, Salt and Pepper and Speckle noise.read more
Citations
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Journal ArticleDOI
A comparative study of image filtering on various noisy pixels
TL;DR: A detail study of the research work done in the field of image filtering is required in order to design a filter which will fulfill the desire aspects along with handling most of the image filtering issues.
Proceedings ArticleDOI
Multilevel-DWT based image de-noising using feed forward artificial neural network
TL;DR: This work presents an approach to de-noise images by combining the features of multilevel Discrete Wavelet Transform (DWT) and Feed Forward Artificial Neural Network (FF ANN) and shows that the proposed method proves effective for a range of variations and is suitable for critical applications.
Proceedings ArticleDOI
An Improved Feature Extraction Method for Texture Classification with Increased Noise Robustness
TL;DR: This paper presents an improved feature extraction method based on the use of state-of-the art filtering techniques and Local Binary Patterns-derived feature descriptors with applications in texture classification that is adaptive, being capable of determining the type of noise present in the input image and applying the appropriate operator for the filtering step of the feature extraction technique.
Book ChapterDOI
AI Based Automated Identification and Estimation of Noise in Digital Images
K. G. Karibasappa,K. Karibasappa +1 more
TL;DR: An automated system for noise identification and estimation technique by adopting the Artificial intelligence techniques such as Probabilistic Neural Network (PNN) and Fuzzy logic concepts and performance is evaluated for classification accuracies.
Book ChapterDOI
A Comparative Study of Video Splitting Techniques
TL;DR: This paper presents the preliminary work on splitting the videos into frames without loss of scrolling textual information and compares the efficiency of the technique against FFmpeg and VLC which are the most widely used techniques for splitting the Videos into frames in-terms of image difference and peak signal to noise ratio (PSNR).
References
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Book
Digital Image Processing Using MATLAB
TL;DR: 1. Fundamentals of Image Processing, 2. Intensity Transformations and Spatial Filtering, and 3. Frequency Domain Processing.
Journal ArticleDOI
Tri-state median filter for image denoising
Tao Chen,Kai-Kuang Ma,Lihui Chen +2 more
TL;DR: A novel nonlinear filter, called tri-state median (TSM) filter, is proposed for preserving image details while effectively suppressing impulse noise by balancing the tradeoff between noise reduction and detail preservation.
Proceedings ArticleDOI
Speckle filtering of SAR images: a comparative study between complex-wavelet-based and standard filters
Langis Gagnon,Alexandre Jouan +1 more
TL;DR: A comparative study between a complex Wavelet Coefficient Shrinkage filter and several standard speckle filters that are widely used in the radar imaging community finds that the WCS filter performs equally well as the standard filters for low- level noise and slightly outperforms them for higher-level noise.
Journal ArticleDOI
Edge-adaptive Kalman filtering for image restoration with ringing suppression
TL;DR: It is shown that ringing artifacts can be suppressed to a great extent by using multiple image models that provide a better match to local edge orientation in the edge-adaptive RUKF.
Journal ArticleDOI
Impulse noise detection and removal using fuzzy techniques
David Zhang,Zhou Wang +1 more
TL;DR: Experimental results show that the algorithm is capable of providing significant improvement over many published techniques in terms of both subjective and objective evaluations.