Image dehazing using morphological opening, dilation and Gaussian filtering

Salazar-Colores, Sebastián; Ramos-Arreguín, Juan Manuel; Ortiz-Echeverri, César J.; Cabal-Yépez, Eduardo; Pedraza-Ortega, Jesús-Carlos; Rodríguez‐Reséndiz, Juvenal

doi:10.1007/s11760-018-1286-9

Cited by 30 publications

(9 citation statements)

References 21 publications

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“…The goal of this phase is to emphasise useful image information while reducing unwanted data. Furthermore, the data collected have many error or noise, so that it needs to perform the data preprocessing operation [20,21].…”

Section: Pre-processingmentioning

confidence: 99%

Indonesian license plate recognition with improved horizontal-vertical edge projection

Nurhaida

Nududdin

Ramayanti

2021

IJEECS

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<p>License plate recognition (LPR) is one of the classical problems in the field of object recognition. Its application is very crucial in the automation of transportation system since it helps to recognise a vehicle identity, which information is stored in the license plate. LPR usually consists of three major phases: pre-processing, license plate localisation, optical character recognition (OCR). Despite being classical, its implementation faced with much more complicated problems in the real scenario. This paper proposed an improved LPR algorithm based on modified horizontal-vertical edge Projection. The method uses for detecting and localising the region of interest. It is done using the horizontal and vertical projection of the image. Related works proved that the modified horizontal-vertical edge projection is the simplest method to be implemented, yet very effective against Indonesian license plate. However, its performance gets reduced when specular reflection occurs in the sample image. Therefore, morphological operations are utilised in the pre-processing phase to reduce such effects while preserving the needed information. Eighty sample images which captured using various camera configurations were used in this research. Based on the experimental results, our proposed algorithm shows an improvement compared with the previous study and successfully detect 71 license plates in 80 image samples which results in 88.75% accuracy.</p>

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Section: Pre-processingmentioning

confidence: 99%

Indonesian license plate recognition with improved horizontal-vertical edge projection

Nurhaida

Nududdin

Ramayanti

2021

IJEECS

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“…Step 3. Objects were extracted [19] by labeling the 8-connected objects, the leaf shape image was taken as the largest-area object, and the expansion operation and corrosion operation [20] were performed with the structure element of around plate with a radius of 3 pixels to smooth the leaf image.…”

Section: Plant Materials and Leaf Descriptionmentioning

confidence: 99%

Leaf shape simulation of castor bean and its application in nondestructive leaf area estimation

Wei¹,

Li²,

Li³

et al. 2019

International Journal of Agricultural and Biological Engineering

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The leaf shape and leaf area measurement are crucial in plant growth modeling. The castor bean leaf is large, palm-shaped with multiple clefts. The leaf shape simulation and leaf area estimation were less studied. The circular model and nonrectangular hyperbolic model were developed to describe the standard leaf shape of castor bean in this study, providing a model for simulating the leaf shape and a nondestructive way for estimating the leaf area respectively. In addition, a formula was established to estimate the leaf area by the parameter of the standard leaf shape of castor bean. Based on validation results, the circular model fits the landmarks and nonrectangular hyperbolic model fits the lobe margins very well. The leaf area was accurately estimated by using the established formula. This study could provide a theoretical reference for leaf visualization, a nondestructive and easy way to estimate the leaf area for other complex leaves with multiple lobes.

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“…In addition, the most widely used metrics in the literature for dehazing algorithms are the PSNR and the SSIM index [4]. In general, a higher PSNR and SSIM index refer to a greater level of accuracy in the reconstructed image [38]. The PSNR is defined as the ratio between the power of a signal and the corrupting noise that affects the fidelity of its representation.…”

Section: Evaluation and Customisationmentioning

confidence: 99%

Statistical multidirectional line dark channel for single‐image dehazing

et al. 2019

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Outdoor scenes often contain atmospheric degradation, such as fog or haze, which deteriorate the performance of tracking, autonomous driving and surveillance systems, among others, making dehazing methods an area of considerable interest. However, some dehazing techniques are computationally demanding, generating a trade‐off between time‐consumption and restoration quality. A new method is proposed for improving outdoor images taken with haze effects while making them less time‐consuming. The proposed method was inspired by the Radon transform and tailored for dehazing images by computing the dark channel, in addition to using statistical computations and a heuristic approach to avoid saturated areas. The results obtained were subjected to a reduced‐reference image quality dehazing assessment, a full‐reference metrics Structural SIMilarity (SSIM) index and peak signal‐to‐noise ratio (PSNR) over real‐world and synthetic outdoor images. The results demonstrate that the proposed method presents an adequate balance between new visible edges, increased gradient and saturated pixels, in addition to obtaining at least a 5% increase in the SSIM index and, a 16% increase in the PSNR index, as well as being 5.37 times faster than the four dehazing methods recently introduced in the literature.

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Image dehazing using morphological opening, dilation and Gaussian filtering

Cited by 30 publications

References 21 publications

Indonesian license plate recognition with improved horizontal-vertical edge projection

Indonesian license plate recognition with improved horizontal-vertical edge projection

Leaf shape simulation of castor bean and its application in nondestructive leaf area estimation

Statistical multidirectional line dark channel for single‐image dehazing

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