636 research outputs found
Dehazed Image Quality Evaluation: From Partial Discrepancy to Blind Perception
Image dehazing aims to restore spatial details from hazy images. There have
emerged a number of image dehazing algorithms, designed to increase the
visibility of those hazy images. However, much less work has been focused on
evaluating the visual quality of dehazed images. In this paper, we propose a
Reduced-Reference dehazed image quality evaluation approach based on Partial
Discrepancy (RRPD) and then extend it to a No-Reference quality assessment
metric with Blind Perception (NRBP). Specifically, inspired by the hierarchical
characteristics of the human perceiving dehazed images, we introduce three
groups of features: luminance discrimination, color appearance, and overall
naturalness. In the proposed RRPD, the combined distance between a set of
sender and receiver features is adopted to quantify the perceptually dehazed
image quality. By integrating global and local channels from dehazed images,
the RRPD is converted to NRBP which does not rely on any information from the
references. Extensive experiment results on several dehazed image quality
databases demonstrate that our proposed methods outperform state-of-the-art
full-reference, reduced-reference, and no-reference quality assessment models.
Furthermore, we show that the proposed dehazed image quality evaluation methods
can be effectively applied to tune parameters for potential image dehazing
algorithms
Multi-Scale Fusion of Enhanced Hazy Images Using Particle Swarm Optimization and Fuzzy Intensification Operators
Dehazing from a single image is still a challenging task, where the thickness of the haze depends on depth information. Researchers focus on this area by eliminating haze from the single image by using restoration techniques based on haze image model. Using haze image model, the haze is eliminated by estimating atmospheric light, transmission, and depth. A few researchers have focused on enhancement based methods for eliminating haze from images. Enhancement based dehazing algorithms will lead to saturation of pixels in the enhanced image. This is due to assigning fixed values to the parameters used to enhance an image. Therefore, the enhancement based methods fail in the proper tuning of the parameters. This can be overcome by optimizing the parameters that are used to enhance the images. This paper describes the research work carried to derive two enhanced images from a single input hazy image using particle swarm optimization and fuzzy intensification operators. The two derived images are further fused using multi-scale fusion technique. The objective evaluation shows that the entropy of the haze eliminated images is comparatively better than the state-of-the-art algorithms. Also, the fog density is measured using an evaluator known as fog aware density evaluator (FADE), which considers all the statistical parameters to differentiate a hazy image from a highly visible natural image. Using this evaluator we found that the density of the fog is less in our proposed method when compared with enhancement based algorithms used to eliminate haze from images
Study a quality of the Hazy image by using YIQ color space
Determining the quality of the hazy image is difficult problem, thus these images need to analyzing after determined the quality or dehazing. In this paper, we analyzed the hazy(by the dust) images depending on YIQ color space. First we designed the system captured images which graded for high to very low hazy (by adding the dust) by using HeNe laser, in these images we calculated the Normalize Mean Square error (NMSE) for each components in YIQ and RGB color space, and the basic components in the Structure Similarity Index (SSIM) are (contrast, structure and luminance) moreover the mean for all has been calculated. We can see the lightness (in YIQ) and luminance ( in SSIM) component are not effected by the dust whereas the chromatic components are highly effected by the dust. Keywords: The dust images, Dehazing , YIQ color space , Luminanc
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