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A Detailed Study on Image Denoising Algorithms by Using the Discrete Wavelet Transformation
The seeking for an efficient image denoising methods is still a valid challenge at the researches field of image analysis and processing. In spite of the sophistication and extreme researches in theExpand
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Natural image noise level estimation based on local statistics for blind noise reduction
TLDR
This study proposes an automatic noise estimation method based on local statistics for additive white Gaussian noise. Expand
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Denoising of natural images through robust wavelet thresholding and genetic programming
TLDR
We present a nonlinear filtering method based on a two-step switching scheme to remove both salt-and-pepper and additive white Gaussian noises in an image. Expand
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Additive noise reduction in natural images using second-generation wavelet transform hidden Markov models
Noise reduction or denoising is required for visual improvement or as a preprocessing step for subsequent processing tasks, such as image compression and analysis. Therefore, denoising has become aExpand
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Recognition system for leaf images based on its leaf contour and centroid
TLDR
In this study, an image processing algorithm in order to find out the shape structure of tested plants is presented to be applied as classifier to the plant's leaf. Expand
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Single image dehazing using second-generation wavelet transforms and the mean vector L2-norm
TLDR
In this paper, a novel single image haze removal technique based on edge and fine texture preserving is introduced. Expand
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Natural image noise removal using nonlocal means and hidden Markov models in transform domain
TLDR
In this paper, the use of clustering based on moment invariants and the hidden Markov model (HMM) is proposed to achieve preclassification and thus capture the dependency between additive white Gaussian noise pixel and its neighbors on the wavelet transform. Expand
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Clustering-based natural image denoising using dictionary learning approach in wavelet domain
TLDR
A clustering-based natural image denoising using dictionary learning algorithm in wavelet domain is proposed using second-generation wavelet clustering coefficients in the decomposition levels. Expand
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Natural image noise removal using non local means and hidden Markov models in stationary wavelet transform domain
TLDR
This paper uses the clustered batches of noisy images and hidden Markov models in order to achieve noiseless images where the dependency between additive noise model pixels and its neighbors on stationary wavelet transform is found using HMMs. Expand
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