A Framework for Outdoor RGB Image Enhancement and Dehazing

Chaudhry, Alina Majeed; Riaz, Muhammad Mohsin; Ghafoor, Abdul

doi:10.1109/lgrs.2018.2814016

Cited by 31 publications

(4 citation statements)

References 22 publications

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“…Chaudhry et al. [25] proposed a framework to remove haze in outdoor images based on median filtering and Laplacian filtering. Guo et al.…”

Section: Related Workmentioning

confidence: 99%

“…Liu et al [24] classified image dehazing methods into three categories: image enhancement based on image processing algorithms with hand-crafted features, image dehazing algorithms based on prior information or physical model, and deep learning image dehazing networks. Chaudhry et al [25] proposed a framework to remove haze in outdoor images based on median filtering and Laplacian filtering. Guo et al [26] observed the correlation between adjacent bands in multi-spectral remote sensing images.…”

Section: Image Enhancement For Survey Imagementioning

confidence: 99%

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Enhanced three‐dimensional model reconstruction based on local ternary pattern‐guided fusion of multi‐exposure images

Chan

Leung

et al. 2023

IET Image Processing

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Computer vision applications usually rely on the features extracted from input images with good visibility. Image acquisition systems may produce degraded images with low contrast or distorted colours. For instance, bad weather (haze, fog) can cause images captured outdoor with low visibility. Image processing algorithms generally assume that the input image is the scene radiance. Haze removal, with the recovery of image radiance, ensures reliable features extracted from images and the image processing algorithm can achieve optimal performance. Inspired by the concept of image dehazing, the authors propose an image enhancement method that can be used to improve the visibility of the images. Each original image is first transformed into multiple exposure images by means of gamma‐correction operations and adaptive histogram equalization. The transformed images are analyzed by the computation of the local ternary pattern. The image is then enhanced, with each pixel generated from the set of transformed image pixels weighted by a function of the local pattern feature. The authors evaluate their proposed method on four benchmark image dehazing datasets. The quantitative results show that our method outperforms many deterministic algorithms and deep learning models. Moreover, the authors investigate the impact of image enhancement on a practical image‐based application—the reconstruction of three‐dimensional (3D) model of survey scene. Accurate 3D model reconstruction depends on high‐quality images. Degraded images will result in large errors in the reconstructed 3D model. Experimentations have been carried out on outdoor and indoor surveys. Our analysis finds that when fed into the photogrammetry software, the images enhanced by the authors’ method can reconstruct 3D scene models with sub‐millimetre mean errors, which are much better than those with the original images. As shown in the visual and quantitative results of 3D model reconstruction, the authors’ proposed method also outperforms other image enhancement methods.

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“…Chaudhry et al. [25] proposed a framework to remove haze in outdoor images based on median filtering and Laplacian filtering. Guo et al.…”

Section: Related Workmentioning

confidence: 99%

Section: Image Enhancement For Survey Imagementioning

confidence: 99%

Enhanced three‐dimensional model reconstruction based on local ternary pattern‐guided fusion of multi‐exposure images

Chan

Leung

et al. 2023

IET Image Processing

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“…This algorithm is a feasible and effective method for haze removal of urban RS images and has a good application and promotion value. Chaudhry et al [43] proposed a framework for image restoration and haze removal. It uses hybrid median filtering and accelerated local Laplacian filtering to dehaze the image and has achieved good results on outdoor RGB images and RS images.…”

Section: Remote Sensing Image Dehazing Based On Image Enhancementmentioning

confidence: 99%

A Review of Remote Sensing Image Dehazing

Liu

Wang

et al. 2021

Sensors

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Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, numerous dehazing work and efforts have been made recently, whose strategy is to directly restore single hazy data without the need for using any extra information. In this paper, we first classify the current available algorithm into three categories, i.e., image enhancement, physical dehazing, and data-driven. The advantages and disadvantages of each type of algorithm are then summarized in detail. Finally, the evaluation indicators used to rank the recovery performance and the application scenario of the RS data haze removal technique are discussed, respectively. In addition, some common deficiencies of current available methods and future research focus are elaborated.

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“…In the literature, many color image processing models are proposed based on different color spaces, e.g. color image restoration [2,4,9,13,28,30,36], color image enhancement [9,15] based on RGB color space, color image restoration [8], color image segmentation [10,20,39] based on HSV color space, color image restoration [22], color image enhancement [1] based on CIELAB color space, color image enhancement [33], color image restoration [19,21,23,25,32,34], color image decomposition [26,37] based on opponent color space, etc.…”

Section: Introductionmentioning

confidence: 99%

Total Variations for Hue, Saturation, and Value of a Color Image

Wei Wang,

Chengyun Yang

2024

CSIAM-AM

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In this paper, we develop total variations for hue, saturation, and value of a color image, and we propose a novel hue-saturation-value total variation model for color image restoration. We first refine the hue formulation of a color image to make it mathematically and applicationally meaningful by assigning different hue values to different colors. We then develop the proposed hue-saturation-value total variation based on the conception of hue/saturation/value gradient. We investigate the dual formulation and the properties of the proposed hue-saturation-value total variation, and we finally propose a color image restoration model which is formulated by combing the proposed hue-saturation-value total variation regularization with the data-fitting term between the objective color image and the observed color image. We develop an efficient alternating iterative algorithm to solve the proposed optimization model in practice, and we give the convergence analysis of the proposed algorithm. Numerical examples are presented to demonstrate that the performance of the proposed hue-saturation-value total variation and the proposed color restoration model is better than that of other testing methods in terms of visual quality, peak signal-tonoise ratio (PSNR), structural similarity index (SSIM), and S-CIELAB color error.

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A Framework for Outdoor RGB Image Enhancement and Dehazing

Cited by 31 publications

References 22 publications

Enhanced three‐dimensional model reconstruction based on local ternary pattern‐guided fusion of multi‐exposure images

Enhanced three‐dimensional model reconstruction based on local ternary pattern‐guided fusion of multi‐exposure images

A Review of Remote Sensing Image Dehazing

Total Variations for Hue, Saturation, and Value of a Color Image

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