Localization of radiance transformation for image dehazing in wavelet domain

Khan, Hira; Sharif, Muhammad; Bibi, Nargis; Usman, Muhammad; Haider, Sajjad; Zainab, Saıra; Shah, Jamal Hussain; Bashir, Yasir; Muhammad, Nazeer

doi:10.1016/j.neucom.2019.10.005

Cited by 36 publications

(11 citation statements)

References 39 publications

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“…ese approaches are DCP [50], CTT [51], CNN [52], WT [53], FVID [54], L 1 norm [55], and TGV [56]. Fifteen benchmark synthetic and real-life hazy images are considered for experimental analysis.…”

Section: Performance Analysismentioning

confidence: 99%

See 1 more Smart Citation

Efficient Single Image Dehazing Model Using Metaheuristics-Based Brightness Channel Prior

Kehar

Chopra²,

Singh

et al. 2021

Mathematical Problems in Engineering

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Haze degrades the spatial and spectral information of outdoor images. It may reduce the performance of the existing imaging models. Therefore, various visibility restoration models approaches have been designed to restore haze from still images. But restoring the haze is an open area of research. Although the existing approaches perform significantly better, they are not so effective against a large haze gradient. Also, the effect of hyperparameters tuning issue is also ignored. Therefore, a brightness channel prior (BCP) based dehazing model is proposed. The gradient filter is utilized to improve the transmission map computed using the gradient filter. Nondominated Sorting Genetic Algorithm is also used to optimize the initial parameters of the BCP approach. The comparative analysis shows that BCP performs effectively across a wide range of haze degradation levels without causing any visible artifacts.

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Section: Performance Analysismentioning

confidence: 99%

“…CNN [52] and TGV [56] provide better results, but still some texture distortion is there. WT [53], L 1 norm [55], and FVID [54] suffer from some halo and gradient-reversal artifacts.…”

Section: Visual Analyses Of Proposed Dehazing Modelmentioning

confidence: 99%

Efficient Single Image Dehazing Model Using Metaheuristics-Based Brightness Channel Prior

Kehar

Chopra²,

Singh

et al. 2021

Mathematical Problems in Engineering

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“…We propose a new framework that adds semantically related terms to the keywords extracted from the analyzed documents using semantic analysis and metrics based on visual features. The idea of exploiting visual information extracted from digital images for the task at hand comes from the proven effectiveness of such kind of information for improving performances in several application domains (e.g., images denoising [35,50] and dehazing [40], early diagnosis of leukemia [52] and detection of cerebral aneurysm [39], face recognition [27], perception in autonomous-driving systems [38,44], augmented reality in sports [43]).…”

Section: Introductionmentioning

confidence: 99%

Using a multimedia semantic graph for web document visualization and summarization

Rinaldi

Russo

2020

Multimed Tools Appl

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The synthesis process of document content and its visualization play a basic role in the context of knowledge representation and retrieval. Existing methods for tag-clouds generations are mostly based on text content of documents, others also consider statistical or semantic information to enrich the document summary, while precious information deriving from multimedia content is often neglected. In this paper we present a document summarization and visualization technique based on both statistical and semantic analysis of textual and visual contents. The result of our framework is a Visual Semantic Tag Cloud based on the highlighting of relevant terms in a document using some features (font size, color, etc.) showing the importance of a term compared to other ones. The semantic information is derived from a knowledge base where concepts are represented through several multimedia items. The Visual Semantic Tag Cloud can be used not only to synthesize a document but also to represent a set of documents grouped by categories using a topic detection technique based on textual and visual analysis of multimedia features. Our work aims at demonstrating that with the help of semantic analysis and the combination of textual and visual features it is possible to improve the user knowledge acquisition by means of a synthesized visualization. The whole strategy has been evaluated by means of a ground truth and compared with similar approaches. Experimental results show the effectiveness of our approach, which outperforms state-of-art algorithms in topic detection combining both visual and semantic information.

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“…A lot of enhancement-based defogging algorithms are also researched by many scientists, for example, retinexbased algorithms [17][18][19][20], wavelet-based algorithms [21][22][23][24], etc. Hu [17] enhanced the fog image with enlarging the local contrast.…”

Section: Introductionmentioning

confidence: 99%

“…the area of a small image region, ( , ) of a small image region. The ratio R of v-axis and u-axis can be obtained by formula(21).…”

mentioning

confidence: 99%

An Anisotropic Gaussian Filtering Model for Image De-Hazing

Liu

Chen

et al. 2020

IEEE Access

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This paper proposes a de-hazing algorithm on the basis of the anisotropic Gaussian filtering method to overcome some essential limitations of the DCP-based (the dark channel prior, DCP) methods, such as halo artifacts and over-saturation problems. In this method, the approximate range of the global atmospheric light A is obtained by using the spatial LOG edge detection method, and the accurate A is acquired by combing binary algorithm. And an anisotropic Gaussian filtering method is adopted to optimize the transmission, which can smooth the rough transmission map, improve the clarity of image details and inhibit halo artifact and over-saturation effect. With the accurate A and optimized transmission, the dehazing results can be acquired on the basis of the atmospheric scattering model. The processing effect is verified by our own fog image data set constructed in real life or downloaded from the Internet. The visual effect of our method is more natural in brightness, contrast and detail recovery. Besides, the indicators SSIM, PSNR, Lum, Con, Inf, e and r are relatively high, and indicators MSE and ɛ are relatively low. Therefore, subjective and objective experimental results demonstrate that our method outperforms four state-of-the-art methods in terms of visual sense definition, robustness and time efficiency.

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Localization of radiance transformation for image dehazing in wavelet domain

Cited by 36 publications

References 39 publications

Efficient Single Image Dehazing Model Using Metaheuristics-Based Brightness Channel Prior

Efficient Single Image Dehazing Model Using Metaheuristics-Based Brightness Channel Prior

Using a multimedia semantic graph for web document visualization and summarization

An Anisotropic Gaussian Filtering Model for Image De-Hazing

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