The Importance of Dithering Technique Revisited With Biomedical Images—A Survey

Liu, Yue; Ganesan, P.; Sathish, B. S.; Manikandan, C.; Niranjan, A; Elamaran, V.; Hussein, Ahmed Faeq

doi:10.1109/access.2018.2888503

Cited by 10 publications

(3 citation statements)

References 16 publications

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“…A common application of dithering on image data is halftoning, i.e., displaying images as monochromatic or using one bit per channel. Dithering techniques exploit properties in the spatial integration produced in the human visual system by distributing the errors among pixels, thus reducing the quantisation effects [12].…”

Section: B Ditheringmentioning

confidence: 99%

Fast Blue-Noise Generation via Unsupervised Learning

Giunchi

Sztrajman

Steed

2022

2022 International Joint Conference on Neural Networks (IJCNN)

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Blue noise is known for its uniformity in the spatial domain, avoiding the appearance of structures such as voids and clusters. Because of this characteristic, it has been adopted in a wide range of visual computing applications, such as image dithering, rendering and visualisation. This has motivated the development of a variety of generative methods for blue noise, with different trade-offs in terms of accuracy and computational performance. We propose a novel unsupervised learning approach that leverages a neural network architecture to generate blue noise masks with high accuracy and real-time performance, starting from a white noise input. We train our model by combining three unsupervised losses that work by conditioning the Fourier spectrum and intensity histogram of noise masks predicted by the network. We evaluate our method by leveraging the generated noise for two applications: grayscale blue noise masks for image dithering, and blue noise samples for Monte Carlo integration.

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Section: B Ditheringmentioning

confidence: 99%

Fast Blue-Noise Generation via Unsupervised Learning

Giunchi

Sztrajman

Steed

2022

2022 International Joint Conference on Neural Networks (IJCNN)

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“…Dithering is unavoidable occasionally for display if the picture must be presented with a limited number of colors on equipment. Particularly, newspapers do contain only black (considering 0) and white (considering 1) colors to display an image, which is so-called "half toning" [7]. As a real-time example, in cell phones, the actual 24-bit RGB color image is reduced to an 8-bit RGB format.…”

Section: Introductionmentioning

confidence: 99%

Dithering techniques and segmentation analysis on a mammogram breast cancer image

S.,

R.,

et al. 2023

ACE

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The objective of the publishing industry is to minimize the number of colors in an image for printing. Also, to retain the better quality from the picture, some useful techniques are needed with the aid of only a few colors. The contouring consequences occur when an image quantized bluntly regardless of the quantization, either uniform or non-uniform. To alleviate the issues of contouring, a slight amount of random noise is added (dithered) to the original image, followed by the quantization method. The methods such as dithering and half toning are extensively used strategies in acquiring texts and pictures in books, newspapers, magazines, computer monitors, and LCDs. This article investigates the dithering technique on a mammogram image with different procedures, such as dithering followed by quantization and dithering, quantization followed by filtering are detailed with Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) results. For better visualization, segmentation techniques are applied to the dithering technique.

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“…This study aims to investigate the relationship between HRV and SpO 2 in preterm infants. In particular, the usefulness of various combinations features of HRV and SpO 2 for the estimation of health outcome term is assessed and the predictive power of the sepsis monitoring and explanations that leads to improve the treatment plans inside the hospitals and get more accurate interpretations (Yue et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Characterization of heart rate variability and oxygen saturation in sepsis patients

Al-Mualemi

2021

Expert Systems

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One of the most major and common health crises which occur across all the hospitals, worldwide, is seen to be sepsis that occurs in patients. However, despite its wide prevalence no novel tool has been devised for predicting its occurrence. An accurate and early prediction of sepsis in the patients could significantly help the physicians administer proper treatment and decrease the uncertain diagnosis. Some machinelearning-based models or schemes can help in identifying the potential clinical variables and display a better performance compared to the prevailing conventional lowperformance models. In this study, a machine learning-based scheme for fast and accurate sepsis identification was proposed. This scheme employed the power spectrum and mean estimation for data record intervals, which were then classified for reaching the final decision. For a 72-h interval, the obtained detection accuracy was 94.2% that shows very good sign to use it as a fast and robust sepsis identification.

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The Importance of Dithering Technique Revisited With Biomedical Images—A Survey

Cited by 10 publications

References 16 publications

Fast Blue-Noise Generation via Unsupervised Learning

Fast Blue-Noise Generation via Unsupervised Learning

Dithering techniques and segmentation analysis on a mammogram breast cancer image

Characterization of heart rate variability and oxygen saturation in sepsis patients

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