Very Low Illumination Image Enhancement via Lightness Mapping

Hashim, Ahmed Rafid; Kareem, Hana H.; Daway, Hazim G.

doi:10.1007/978-981-19-6634-7_20

Cited by 3 publications

(5 citation statements)

References 26 publications

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“…6. We observed from the distribution of the histogram that the best distribution rates were for the suggested method, followed by the methods [8,9]. Fig.…”

Section: Resultsmentioning

confidence: 80%

“…Utilizing a method that involves gathering the median and minimum filters for each color component, color restoration can be accomplished. As shown in (R, G, and B) [8].…”

Section: Dcp Algorithm and Color Restorationmentioning

confidence: 82%

“…To determine the efficiency of the suggested algorithm, it was compared with several algorithms (Abraham et al [11], Hashim et al [8], Gupta& Agrawal [13], Daway et al [9], Ren et al [12], Abraham et al [8]. Table 1 shows the evaluation of the enhancement using reference and non-reference quality measurements, where we observed that the best contrast was mostly from the proposed method, followed by Daway et al [9] and Hashimet al [8] methods. As for the NIQE scale, the proposed method succeeded in restoring the color information, as it obtained the best evaluation.…”

Section: Resultsmentioning

confidence: 98%

“…Challenges and future directions are briefly presented in this paper by Yu Wang et al [7]. Hashim et al [8] suggested an approach to improve images taken in a very low light. There are two steps in this method.…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of Lightness Image Based on DCP with Color Restoration and Lightness Mapping

2024

IJIES

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Improving low-light images is used in many medical, military, and other applications, such as classification, detection, and increasing the clarity of targets. In this study, we improved low-light images by relying on three main techniques. The first is (DCP), the second is color retrieval, and the third is improving the lighting component based on the Lab color space, where the color components are isolated from the light, and then mapping is done using intensity stretch and (adapted histogram equalization). LOL data (containing 485 low-light images) were enhanced, with two types of standards being reference peak signal to noise ratio (PSNR) and non-reference scalas as entropy (EN), average gradient (AG), and natural image quality evaluator (NIQE). By analyzing the results, the proposed method obtained an improvement, and the quality standards reached EN (7.550), AG (9.978), NIQE (3.499), and PSNR (15.013) values. Compared to a number of recent algorithms, which indicates its success in increasing the lighting and contrast in these images.

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“…6. We observed from the distribution of the histogram that the best distribution rates were for the suggested method, followed by the methods [8,9]. Fig.…”

Section: Resultsmentioning

confidence: 80%

“…Utilizing a method that involves gathering the median and minimum filters for each color component, color restoration can be accomplished. As shown in (R, G, and B) [8].…”

Section: Dcp Algorithm and Color Restorationmentioning

confidence: 82%

Section: Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Enhancement of Lightness Image Based on DCP with Color Restoration and Lightness Mapping

2024

IJIES

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“…The aerial image is a photographic process carried out from the air, often by using air transports [4]. Aerial images present information on the following: the surface of the earth and its events, as well as the effect of erosion from cutting forests and mountains; archaeological sites; agricultural lands and large pastures, and which ones to be distributed to the public; and natural disasters, for their study and prevention [5]. Kareem et al proposed an algorithm for improving a group of aerial photographs that suffer from the haze.…”

Section: Introductionmentioning

confidence: 99%

Aerial Images Enhancement Using Perceptual Dark Channel Prior

2023

IJIES

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Improvements in aerial images have an important role, given the increase in dust storms due to global warming and climatic changes. Apart from improving aerial images, image enhancement is important in many applications, such as tracking and monitoring studies during wars and for the environment. However, aerial images often suffer from a lack of contrast due to dust and pollutants. In this study, we propose a perceptual dark channel prior (PDCP). In this method, the light value (Y) is improved by using stretch and perceptual color space, and then the chromatic compounds (CbCr) are improved by dark channel prior (DCP). The quality measures lightness order error (LOE), entropy, and color-fullness metrics (CM) and then compares these with the improved methods (DCPMV, YCBCR, MLA, SEA, DCP, and IEIF) to determine the method efficiency. Results show that the proposed PDCP effectively enhances the aerial images better than other methods, because owning it the average are LOE (194.096), EN (7.361), and CM (39.403).

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