Fuzzy color histogram equalization with weighted distribution for image enhancement

A semantic ontology-driven hierarchical consistency segmentation algorithm was proposed to solve the segmentation inconsistency of animation character models because of the changing of poses. The mapping between semantic labels and local geometric features was extracted to form a segmentation ontology. In the process of segmentation, support vector machine (SVM) and local geometric features were used to identify the semantic labels, and segmentation was carried out according to the semantic label driving hierarchy to ensure the consistency of the segmentation levels of animation character models. Poisson equation was used to define contour lines for the equal perimeters of segmentation boundary because of the changing of poses. This optimization method made the segmentation boundary smooth and consistent under the changing of poses. In the experiment part, all kinds of animation character models under different poses were verified and analyzed, and the consistent hierarchical segmentation effect was obtained. Compared with the existing methods, the proposed segmentation ontology can solve the problem of adaptive selection of optimization parameters of different classes of models, and improve the segmentation quality. With the continuous development of deep learning, the use of image segmentation for animation, and human pose recognition will become more and more important.

show abstract

“…Figure 4 shows the histogram distribution of human arm subparts and legs [14]. It can be found that in Figures 4(a…”

Section: Semantic Label Recognitionmentioning

confidence: 97%

Research on Animation Character Based on the Semantic Segmentation Model

Huang¹,

Zhang

Wu³

2022

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…Another intensity based enhancement was presented by Krishnamurthy Mayathevar et al [30], using fuzzy histogram constructed using intensity of neighbourhood regions and further improved by gamma correction in dark regions. Fading effect is mitigated by limiting maximum saturation range as well.…”

Section: Literature Reviewmentioning

confidence: 99%

Image Contrast Optimization using Local Color Correction and Fuzzy Intensification

Dixit¹,

Yadav²,

Mishra³

2021

IJACSA

View full text Add to dashboard Cite

Global image enhancement techniques are used to enhance contrast in images but these techniques are found to be under-enhanced or over-enhanced in differently illuminated regions of the image. Local color correction methods work on local pixel regions to optimize the color contrast enhancement but they also have been found to show a lag while covering pixel regions which are overexposed, compared to those which are underexposed causing local artifacts. In this work, we overcome the shortcomings of both the local color correction and global color correction. This method uses local color correction in the Hue Saturation Luminance (HSL) domain, and fuzzy intensification operators are used to control the color fidelity of the local color corrected images. Thus, is able to sort out the problem of overexposed and underexposed regions and provide optimized contrast enhancement in colored images. Several experiments have been performed to analyze the performance of the proposed method and feasibility as compared to existing techniques. Performance parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM) and Naturalness Image Quality Evaluator (NIQE) is evaluated and the comparison with some existing techniques of contrast enhancement of color images is performed. The obtained result have good contrast and approve the better performance of the proposed method in support of the quantitative measure of perceptual appearance of the processed images and low computational time.

show abstract

“…e traditional histogram equalization (HE) method [6][7][8] is simple and efficient. e haze image has a narrow centralized histogram and low contrast.…”

Section: Introductionmentioning

confidence: 99%

Image Enhancement Algorithm Based on Depth Difference and Illumination Adjustment

Bao

Yuan

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

In order to improve the clarity and color fidelity of traffic images under the complex environment of haze and uneven illumination and promote road traffic safety monitoring, a traffic image enhancement model based on illumination adjustment and depth of field difference is proposed. The algorithm is based on Retinex theory, uses dark channel principle to obtain image depth of the field, and uses spectral clustering algorithm to cluster image depth. After the subimages are divided, the local haze concentration is estimated according to the depth of field and the subimages are adaptively enhanced and fused. In addition, the illumination component is obtained by multiscale guided filtering to maintain the edge characteristics of the image, and the uneven illumination problem is solved by adjusting the curve function. The experimental results show that the proposed model can effectively enhance the uneven illumination and haze weather image in the traffic scene and the visual effect of the images is good. The generated image has rich details, improves the quality of traffic images, and can meet the needs of traffic practical application.

show abstract

Fuzzy color histogram equalization with weighted distribution for image enhancement

Cited by 39 publications

References 21 publications

Research on Animation Character Based on the Semantic Segmentation Model

Research on Animation Character Based on the Semantic Segmentation Model

Image Contrast Optimization using Local Color Correction and Fuzzy Intensification

Image Enhancement Algorithm Based on Depth Difference and Illumination Adjustment

Contact Info

Product

Resources

About