Image Segmentation and Adaptive Contrast Enhancement for Haze Removal

Wang, Chunyan; Bao, Zhu

doi:10.1109/mwscas48704.2020.9184525

Cited by 10 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The basic condition for the generalized sample mean m G to be a local minimum of the objective function (15) is that the gradient of this function with respect to a is equal to zero 29,34 , that is mathematically described as given by Eq. (17).…”

Section: Power Meanmentioning

confidence: 99%

See 1 more Smart Citation

Power mean based image segmentation in the presence of noise

Rahman

Ali

Badshah

et al. 2022

Sci Rep

View full text Add to dashboard Cite

In image segmentation and in general in image processing, noise and outliers distort contained information posing in this way a great challenge for accurate image segmentation results. To ensure a correct image segmentation in presence of noise and outliers, it is necessary to identify the outliers and isolate them during a denoising pre-processing or impose suitable constraints into a segmentation framework. In this paper, we impose suitable removing outliers constraints supported by a well-designed theory in a variational framework for accurate image segmentation. We investigate a novel approach based on the power mean function equipped with a well established theoretical base. The power mean function has the capability to distinguishes between true image pixels and outliers and, therefore, is robust against outliers. To deploy the novel image data term and to guaranteed unique segmentation results, a fuzzy-membership function is employed in the proposed energy functional. Based on qualitative and quantitative extensive analysis on various standard data sets, it has been observed that the proposed model works well in images having multi-objects with high noise and in images with intensity inhomogeneity in contrast with the latest and state-of-the-art models.

show abstract

Section: Power Meanmentioning

confidence: 99%

“…Moreover, the availability of robust implementations, such as efficient optimization, and fast numerical methods is crucial. The main idea in active contour methods 1,[7][8][9][10][12][13][14][15][16][17] is to allow dynamical curves to move autonomously on a given image which locates boundaries of the objects/regions therein.…”

Section: Introductionmentioning

confidence: 99%

Power mean based image segmentation in the presence of noise

Rahman

Ali

Badshah

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…However, the noise will increase when the histogram slope is steep. [13] has proposed the use of Contrast Limited Adaptive Histogram Equalization (CLAHE) that can overcome the problem in AHE. This is because, in CLAHE, generation of the histogram is devised where after generating a histogram, it is cropped by a predefined threshold and further distributed to the pixels in the histogram.…”

Section: Figurementioning

confidence: 99%

Multi-temporal satellite image correction based on CNN features and adaptive contrast enhancement

Saberi

Hashim

Ali

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

In recent years, identifying changes in multi-temporal images in terms of land use and land cover has been significant in a variety of applications, including urban planning. Due to weather and environmental effects, optical remote sensing has limitations in obtaining images where the image quality may be degraded. It’s because the images being registered are taken at various times, viewpoints, and types of sensors. In this article, the pre-processing methods, which include radiometric correction and geometric correction, are introduced to enhance the quality of satellite images and identify correct spatial alignment. For radiometric correction, adaptive contrast enhancement is done by combining histogram- and non-linear transfer function-based approaches in CIELAB color space. A comparison study is done to see how the new method compared to other methods. For geometric correction, the features from two images are extracted using Convolutional Neural Network to match and align them. The introduced approach for radiometric correction gave the best average rank of BRISQUE scores and RMSE of contrast scores, and the geometric correction can align two images together with an average accuracy of improvement of 91.78 percent. The findings of this research will provide the preliminary step for any change detection activities.

show abstract

“…These methods have the advantage of not requiring a known clear target, as their aim is to solely reduce the hazy effects such as described in Section 2.1. These methods attempt to improve the visibility of the image through methods such as color correction [3,17], contrast correction [2,18], contrast balancing [9,19], and others.…”

Section: Image Dehazingmentioning

confidence: 99%

Evaluation of the quality indicators in dehazed images: Color, contrast, naturalness, and visual pleasingness

Rahadianti

Azizah

Deborah

2021

Heliyon

View full text Add to dashboard Cite

Hazy images suffer from low quality due to blurring, veiling effects, and low contrast. To improve their visibility, dehazing methods attempt to restore them to their corresponding clear scenes, often by focusing more on obtaining an accurate estimate based on a known ground truth. The perceptual quality of dehazed images, which can be described by means of objective and subjective quality assessments, is often not considered. This paper provides a quality assessment of dehazed images, focusing on aspects, e.g., color, image structure, and naturalness. Four image dehazing methods are considered, i.e., Contrast Limited Adapted Histogram Equalization (CLAHE), Dark Channel Prior and Refinement (DCP-R), Perception Inspired Deep Dehazing Network with Refinement (PDR-Net) and Conditional Generative Adversarial Network (CGAN) Pix2pix. The dehazing results are then put through objective and subjective assessments, for a comprehensive evaluation on image quality. Overall, Pix2pix shows the best results objectively, excelling in the recovery of color and image structure. Although it is outperformed by DCP-R in terms of naturalness, our subjective assessment shows that Pix2pix is also most preferred by human observers.

show abstract

Image Segmentation and Adaptive Contrast Enhancement for Haze Removal

Cited by 10 publications

References 19 publications

Power mean based image segmentation in the presence of noise

Power mean based image segmentation in the presence of noise

Multi-temporal satellite image correction based on CNN features and adaptive contrast enhancement

Evaluation of the quality indicators in dehazed images: Color, contrast, naturalness, and visual pleasingness

Contact Info

Product

Resources

About