Color image enhancement using daubechies wavelet transform and HIS color model

Nikam, Swati; Yawale, Rajesh U.

doi:10.1109/iic.2015.7150953

Cited by 4 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In HIS color model, I represents brightness, H represents hue, and S represents saturation [13,14]. When S is large, the tone of the image is very pure, and all regions can be easily separated by H. On the contrary, when S is small, the color of the color image is low and almost becomes grayscale image.…”

Section: Color Information Image Segmentationmentioning

confidence: 99%

Parallax information fusion-based for dance moving image posture extraction

Liu¹,

Teng²

2018

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

The existing motion image posture contour extraction results have low definition and serious detail loss. To solve this problem, we propose a novel dance moving image posture extraction method based on parallax information fusion. Firstly, the image with motion information is statistically analyzed by using the information fusion process to determine the position of the motion region. After the noise is reduced by morphological processing, the initial motion posture profile is obtained. The parallax between different control points and the center is used as the active contour model to shape the contraction force and expansion force, which can effectively assist the initial edge contour curve to gradually approach the real edge contour. Finally, the contour of the current moving image is extracted from the sequence image contour to obtain the attitude contour of the moving image. The experimental results show that the proposed method can extract the contour of the moving image clearly with less detail loss, which proves that the proposed method has strong practical performance and can effectively find the contour of the moving object.

show abstract

Section: Color Information Image Segmentationmentioning

confidence: 99%

Parallax information fusion-based for dance moving image posture extraction

Liu¹,

Teng²

2018

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

show abstract

“…Reference [4] proposed an efficient transformation-free approach for color image enhancement, which manipulates pixels value directly in source RGB color space. Reference [5] presented a color image enhancement method using daubechies wavelet transform and HIS color space. Reference [6] enhanced the pathological anatomy images based on superresolution to improve medical diagnosis.…”

Section: Introductionmentioning

confidence: 99%

HDR Pathological Image Enhancement Based on Improved Bias Field Correction and Guided Image Filter

Sun

Jiang

Zhu

et al. 2016

BioMed Research International

View full text Add to dashboard Cite

Pathological image enhancement is a significant topic in the field of pathological image processing. This paper proposes a high dynamic range (HDR) pathological image enhancement method based on improved bias field correction and guided image filter (GIF). Firstly, a preprocessing including stain normalization and wavelet denoising is performed for Haematoxylin and Eosin (H and E) stained pathological image. Then, an improved bias field correction model is developed to enhance the influence of light for high-frequency part in image and correct the intensity inhomogeneity and detail discontinuity of image. Next, HDR pathological image is generated based on least square method using low dynamic range (LDR) image, H and E channel images. Finally, the fine enhanced image is acquired after the detail enhancement process. Experiments with 140 pathological images demonstrate the performance advantages of our proposed method as compared with related work.

show abstract