Using wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images

Boix, Macarena; Cantó, Begoña

doi:10.3934/mbe.2013.10.279

Cited by 4 publications

(3 citation statements)

References 23 publications

(21 reference statements)

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“…The High-Pass filter that forms the wavelet function generates approximations A for each decomposition level. Details of D (Mallat, 1999) (Boix & Cantó, 2013) are provided by the complementary low-pass filter representing the scaling function. This algorithm is referred to as subband coding.…”

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

Security Inspection Image Processing Methods Applying Wavelet Transform Filters on Terahertz Active Images

Danso

Shang

Deng

et al. 2022

Rev. Invest. Univ. Quindío

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Clarity of Terahertz image is essential at various security checkpoints to avoid life’s dangers and treats. However, Terahertz images are distorted by noise. Noise is frequently present in digital images during the image collection, coding, delivery, and processing phases. It is extremely difficult to remove noise from digital images without prior knowledge of the noise model. Wavelet transforms have gained popularity as a tool for image denoising. In this paper, we advance a solution to this challenge using Global Threshold selection as well as wavelet transform filters. When compared to denoising Gaussian noise at the same percentage induced, biorthogonal is the most effective denoising filter for salt and pepper noise. As the salt and pepper noise increases from 20% to 60%, the hidden security image as our target varnishes or is overpowered by the induced salt and pepper noise. We discover that despite the fact that the bior 4.4 and sym4.0 wavelet transform filters prove powerful in denoising the image, it is still not clearer and that when an image is tainted by Gaussian noise, wavelet shrinkage denoising is nearly perfect in both bior4.4 and sym 4.0, whereas when the image is tainted by salt & pepper noise, wavelet shrinkage denoising is nearly perfect in both bior4.4 and sym 4.0.

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Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

Security Inspection Image Processing Methods Applying Wavelet Transform Filters on Terahertz Active Images

Danso

Shang

Deng

et al. 2022

Rev. Invest. Univ. Quindío

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“…In fact, Gauss de-noising has attracted the attention of a large number of researchers, and many de-noising algorithms have been presented that achieved good results in many research fields [7,8]. However, the Gauss de-noising is still a problem [8] and its performance is not ideal in real applications.…”

Section: Introductionmentioning

confidence: 99%

“…These have a great impact on the visual system of an apple harvesting robot, and make it difficult to recognize targets [5,6]. Through the subtraction image method, it is showed that the noise in night vision images is generally mixed noise, and the Gauss noise is found to be the main noise among them [7].…”

Section: Introductionmentioning

confidence: 99%

Night Vision Image De-noising of Apple Harvesting Robots Based on the Wavelet Fuzzy Threshold

Ruan

Zhao

Jia

et al. 2015

Int. j. adv. robot. syst.

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In this paper, the de-noising problem of night vision images is studied for apple harvesting robots working at night. The wavelet threshold method is applied to the de-noising of night vision images. Due to the fact that the choice of wavelet threshold function restricts the effect of the wavelet threshold method, the fuzzy theory is introduced to construct the fuzzy threshold function. We then propose the de-noising algorithm based on the wavelet fuzzy threshold. This new method can reduce image noise interferences, which is conducive to further image segmentation and recognition. To demonstrate the performance of the proposed method, we conducted simulation experiments and compared the median filtering and the wavelet soft threshold de-noising methods. It is shown that this new method can achieve the highest relative PSNR. Compared with the original images, the median filtering de-noising method and the classical wavelet threshold de-noising method, the relative PSNR increases 24.86%, 13.95%, and 11.38% respectively. We carry out comparisons from various aspects, such as intuitive visual evaluation, objective data evaluation, edge evaluation and artificial light evaluation. The experimental results show that the proposed method has unique advantages for the denoising of night vision images, which lay the foundation for apple harvesting robots working at night.

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Deploying Wavelet Transforms in Enhancing Terahertz Active Security Images

Zhang

Shang

et al. 2022

Informatics and Intelligent Applications

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Using wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images

Cited by 4 publications

References 23 publications

Security Inspection Image Processing Methods Applying Wavelet Transform Filters on Terahertz Active Images

Security Inspection Image Processing Methods Applying Wavelet Transform Filters on Terahertz Active Images

Night Vision Image De-noising of Apple Harvesting Robots Based on the Wavelet Fuzzy Threshold

Deploying Wavelet Transforms in Enhancing Terahertz Active Security Images

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