Satellite Image Enhancement Using an Ameliorated Balance Contrast Enhancement Technique

Al-Ameen, Zohair

doi:10.18280/ts.370210

Cited by 10 publications

(6 citation statements)

References 29 publications

(46 reference statements)

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“…The proposed algorithm is expected to process many underwater images and produce results with good color, normal contrast, few impurities, and good brightness. The developments on the CBF are as follows: Firstly, instead of using gamma correction, an ABCETP algorithm developed by [29], is utilized with a TGA process to improve the contrast and brightness. Secondly, instead of using the NUM filter, an ADUSM filter proposed by [30], is used instead to provide a better sharpness.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Low Intricacy‎ Multistage Algorithm for Underwater Image Enhancement

Al-Ameen,

Ahmed

2024

protk

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Humanity currently lives in a technological era that witnesses rapid progress in multiple fields. Digital image processing is one of the modern technologies that has provided practical answers to many challenges including image enhancement, analysis, reconstruction, recovery, compression, processing, and understanding. One of these notable challenges relates to underwater photography. Underwater images are always exposed to less-than-ideal conditions due to environmental and physical factors. These include refraction of light in water, scattering of particles and dust in the aquatic medium, lack of illumination in deep water, and poor contrast. These challenges make it extremely difficult to analyze and extract valuable information without advanced processing. In this study, an improved color balance-fusion algorithm is provided by improving the image visuality and modifying some equations to obtain sharper and clearer images. The proposed algorithm begins by finding the white balance of the input RGB color image, after that, it improves the intensity. Next, the edges are improved using Gamma separately. The weights are then found for each image and combined to find naive fusion. The resulting image is processed using a color retrieval algorithm to produce the final image. along with comparisons to eleven other algorithms with various processing methods. Experimental results showed that this algorithm can significantly improve underwater images, increasing image clarity and making colors clearer. The improvement rates reached 5.8389 and 2.6778 for UISM and UICM metrics, respectively.

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Section: Proposed Algorithmmentioning

confidence: 99%

Low Intricacy‎ Multistage Algorithm for Underwater Image Enhancement

Al-Ameen,

Ahmed

2024

protk

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“…Therefore, the automatic linear contrast stretching (ALCS) procedure is used to supply a natural range stretch by redistributing the pixel values to the entire interval. The following equation is utilized to compute the ALCS approach as [34]:…”

Section: Proposed Adapted Ssr Algorithmmentioning

confidence: 99%

Adapted Single Scale Retinex Algorithm for Nighttime Image Enhancement

Ismail

Al-Ameen

2022

AL-Rafidain Journal of Computer Sciences and Mathematics

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Images captured at night with low-light conditions frequently have a loss of visible details, inadequate contrast, low brightness, and noise. Therefore, it is difficult to perceive, extract, and analyze important visual information from these images, unless they were properly processed. Different algorithms exist to process nighttime images, yet most of these algorithms are highly complex, generate processing artifacts, over-smooth the images, or do not improve the illumination adequately. Thus, the single scale retinex (SSR) algorithm is adopted in this study to provide better processing for nighttime images. The proposed algorithm starts by converting the color image from the RGB model to the HSV model and enhancing the V channel only while preserving the H and S channels. Then, it determined the image's illuminated version somewhat like the SSR, computes the logarithms of the illuminated and original images, then subtracts these two images by utilizing an altered procedure. Next, a modified gamma-adjusted Rayleigh distribution function is applied, and its outcome is processed once more by an automatic linear contrast stretching approach to produce the processed V channel that will be utilized with the preserved H and S channels to generate the output RGB image. The developed algorithm is assessed using a real dataset of nighttime images, evaluated using three dedicated image evaluation methods, and compared to ten dissimilar contemporary algorithms. The obtained results demonstrated that the proposed algorithm can significantly improve the perceptual quality of nighttime images and suppress artifact generation rapidly and efficiently, in addition to showing the ability to surpass the performance of different existing algorithms subjectively and objectively.

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“…Dissimilar kinds of non-contrast computed tomography imaging are regarded for evaluating the proposed algorithm's performance related to contrast enhancement, which depends upon adaptive gamma correction by using Singular Value Decomposition and discrete wavelet transform. In [5] low-contrast effects often degrade satellite image quality. Thus, various researches are conducted to treat this type of effect.…”

Section: Related Workmentioning

confidence: 99%