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

Kehar, Vinay; Chopra, Vinay; Singh, Bhupesh Kumar; Tiwari, Shailendra

doi:10.1155/2021/5584464

Cited by 3 publications

(4 citation statements)

References 58 publications

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“…Figure 10 shows the f-measure analysis of the SPEA-IIbased ATM. In terms of f-measure, SPEA-II-based ATM achieves a mean improvement as 1.178% over the competitive models [48].…”

Section: Expand Populationmentioning

confidence: 96%

Medical Image Captioning Using Optimized Deep Learning Model

Singh

Raguru

Prasad

et al. 2022

Computational Intelligence and Neuroscience

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Medical image captioning provides the visual information of medical images in the form of natural language. It requires an efficient approach to understand and evaluate the similarity between visual and textual elements and to generate a sequence of output words. A novel show, attend, and tell model (ATM) is implemented, which considers a visual attention approach using an encoder-decoder model. But the show, attend, and tell model is sensitive to its initial parameters. Therefore, a Strength Pareto Evolutionary Algorithm-II (SPEA-II) is utilized to optimize the initial parameters of the ATM. Finally, experiments are considered using the benchmark data sets and competitive medical image captioning techniques. Performance analysis shows that the SPEA-II-based ATM performs significantly better as compared to the existing models.

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“…Figure 10 shows the f-measure analysis of the SPEA-IIbased ATM. In terms of f-measure, SPEA-II-based ATM achieves a mean improvement as 1.178% over the competitive models [48].…”

Section: Expand Populationmentioning

confidence: 96%

Medical Image Captioning Using Optimized Deep Learning Model

Singh

Raguru

Prasad

et al. 2022

Computational Intelligence and Neuroscience

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“…The dehazing scheme used in this work [ 27 , 28 ] deals with four pre-defined steps. In the first step, image depth map estimation is performed using the local path shown in Equation (1).…”

Section: Methodsmentioning

confidence: 99%

“…Further detail of this dehazing approach can be seen from [ 27 ]. After dehazing, the image is restored to its original form by using the inverse complement function, as shown in Figure 5 b as a dehazed image.…”

Section: Methodsmentioning

confidence: 99%

Single Channel Image Enhancement (SCIE) of White Blood Cells Based on Virtual Hexagonal Filter (VHF) Designed over Square Trellis

Rasheed

Raza

Sharif

et al. 2022

JPM

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White blood cells (WBCs) are the important constituent of a blood cell. These blood cells are responsible for defending the body against infections. Abnormalities identified in WBC smears lead to the diagnosis of disease types such as leukocytosis, hepatitis, and immune system disorders. Digital image analysis for infection detection at an early stage can help fast and precise diagnosis, as compared to manual inspection. Sometimes, acquired blood cell smear images from an L2-type microscope are of very low quality. The manual handling, haziness, and dark areas of the image become problematic for an efficient and accurate diagnosis. Therefore, WBC image enhancement needs attention for an effective diagnosis of the disease. This paper proposed a novel virtual hexagonal trellis (VHT)-based image filtering method for WBC image enhancement and contrast adjustment. In this method, a filter named the virtual hexagonal filter (VHF), of size 3 × 3, and based on a hexagonal structure, is formulated by using the concept of the interpolation of real and square grid pixels. This filter is convolved with WBC ALL-IBD images for enhancement and contrast adjustment. The proposed filter improves the results both visually and statically. A comparison with existing image enhancement approaches proves the validity of the proposed work.

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“…Kehar et al [22] implemented an image de-hazing model using a metaheuristics-based brightness channel prior. The method involves the evaluation of image brightness, atmospheric light, and transmission map estimation, which then will be applied to the restoration model.…”

Section: Related Workmentioning

confidence: 99%

Digital image enhancement by brightness and contrast manipulation using Verilog hardware description language

Azhari

Setumin

Noorsal

2023

IJECE

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A foggy environment may cause digitally captured images to appear blurry, dim, or low in contrast. This will impact computer vision systems that rely on image information. With the need for real-time image information, such as a plate number recognition system, a simple yet effective image enhancement algorithm using a hardware implementation is very much needed to fulfil the need. To improve images that suffer from low exposure and hazy, the hardware implementations are usually based on complex algorithms. Hence, the aim of this paper is to propose a less complex enhancement algorithm for hardware implementation that is able to improve the quality of such images. The proposed method simply combines brightness and contrast manipulation to enhance the image. In order to see the performance of the proposed method, a total of 100 vehicle registration number images were collected, enhanced, and evaluated. The evaluation results were compared to two other enhancement methods quantitatively and qualitatively. Quantitative evaluation is done by evaluating the output image using peak signal-to-noise ratio and mean-square error evaluation metrics, while a survey is done to evaluate the output image qualitatively. Based on the quantitative evaluation results, our proposed method outperforms the other two enhancement methods.

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

Cited by 3 publications

References 58 publications

Medical Image Captioning Using Optimized Deep Learning Model

Medical Image Captioning Using Optimized Deep Learning Model

Single Channel Image Enhancement (SCIE) of White Blood Cells Based on Virtual Hexagonal Filter (VHF) Designed over Square Trellis

Digital image enhancement by brightness and contrast manipulation using Verilog hardware description language

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