A deep journey into image enhancement: A survey of current and emerging trends

Lepcha, Dawa Chyophel; Goyal, Bhawna; Dogra, Ayush; Sharma, Kanta Prasad; Gupta, Deena Nath

doi:10.1016/j.inffus.2022.12.012

Cited by 21 publications

(6 citation statements)

References 232 publications

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“…The results can be seen in Figure 6g and the histogram of the image (j). The pixels that were made whiter were adjusted by the parameter "γ" of the function (8).…”

Section: Resultsmentioning

confidence: 99%

“…The general model of processing images with fuzzy sets can be depicted in Figure 1 [8] (p. 53). The enhancement system is split into three phases: image fuzzification, image defuzzification, and membership value modification.…”

Section: Structure Of the Fuzzy Image Processing Algorithmmentioning

confidence: 99%

“…Noise and blur distort the appearance of pathology and lead to misclassification. Contrast enhancement techniques could improve contrast, restore pathological features, and, hence, assist in accurate diagnostic decisions [4,5,7,8]. There are many contrast enhancement methods, and the most common is histogram equalization, which maps the spatial histogram of the image to a uniform distribution.…”

Section: Introductionmentioning

confidence: 99%

“…The main purpose of enhancing the contrast of an image is to improve the contrast overall and locally, create an image more suitable for physician observation or computer processing, improve its visual effect, and avoid noise amplification. It creates an image that obtains better results in further processing [8].…”

Section: Introductionmentioning

confidence: 99%

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Advanced Fuzzy Sets and Genetic Algorithm Optimizer for Mammographic Image Enhancement

Dounis,

Avramopoulos,

Kallergi

2023

Electronics

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A well-researched field is the development of Computer Aided Diagnosis (CADx) Systems for the benign-malignant classification of abnormalities detected by mammography. Due to the nature of the breast parenchyma, there are significant uncertainties about the shape and geometry of the abnormalities that may lead to an inaccurate diagnosis. These same uncertainties give mammograms a fuzzy character that is essential to the application of fuzzy processing. Fuzzy set theory considers uncertainty in the form of a membership function, and therefore fuzzy sets can process imperfect data if this imperfection originates from vagueness and ambiguity rather than randomness. Fuzzy contrast enhancement can improve edge detection and, by extension, the quality of related classification features. In this paper, classical (Linguistic hedges and fuzzy enhancement functions), advanced fuzzy sets (Intuitionistic fuzzy set (ΙFS), Pythagorean fuzzy set (PFS), and Fermatean fuzzy sets (FFS)), and a Genetic Algorithm optimizer are proposed to enhance the contrast of mammographic features. The advanced fuzzy sets provide better information on the uncertainty of the membership function. As a result, the intuitionistic method had the best overall performance, but most of the techniques could be used efficiently, depending on the problem that needed to be solved. Linguistic methods could provide a more manageable way of spreading the histogram, revealing more extreme values than the conventional methods. A fusion technique of the enhanced mammography images with Ordered Weighted Average operators (OWA) achieves a good-quality final image.

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“…The results can be seen in Figure 6g and the histogram of the image (j). The pixels that were made whiter were adjusted by the parameter "γ" of the function (8).…”

Section: Resultsmentioning

confidence: 99%

Section: Structure Of the Fuzzy Image Processing Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Advanced Fuzzy Sets and Genetic Algorithm Optimizer for Mammographic Image Enhancement

Dounis,

Avramopoulos,

Kallergi

2023

Electronics

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“…The information entropy increases with the amount of information in the image and the effectiveness of the image display detail effect. Entropy of information can be defined as 21,24 :…”

Section: Simulation and Analysismentioning

confidence: 99%

Research on image enhancement algorithm for the monitoring system in coal mine hoist

Zhang,

Zuo,

Wang

et al. 2023

Measurement and Control

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As the mine hoist monitors video images with poor light, low brightness, heavy dust, and low contrast, the monitoring video images are not conducive to monitoring. They cannot meet the needs of applications. Based on actual video surveillance data, this paper proposes a dark channel prior (DCP) method integrated with a guided image filter video image enhancement algorithm. Firstly, we analyzed the characteristics of the mine hoist system’s video images. Then, the DCP technique was used to enhance the video images. A guided image filter algorithm was introduced to ensure that the video has more clarity and visual impact. Comparing the suggested method to the other four algorithms, it performed better both subjectively and objectively than the algorithms mentioned above. Experiments demonstrate that the proposed technique can successfully improve the entire clarity and contrast of video images while avoiding the over-enhancement of bright areas close to the light source, meeting the practical application requirements of video surveillance.

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A method for detecting navigable areas in narrow rivers under complex reflection conditions

Zhang,

Hu,

et al. 2024

Engineering Reports

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The perception of unmanned surface vehicles is significantly influenced by the detection of navigable areas in narrow rivers. Conventional semantic segmentation networks are unable to resolve the numerous interferences on the water's surface, including highlights and inverted images. To solve this problem, a river surface image reflection removal generative adversarial network (RRGAN) is proposed to eliminate the interference of harsh water surface environment. The proposed RRGAN only uses a single generator to reduce the number of parameters. By adding AdaLIN layers in the generator to enhance the ability to generate low‐reflection images, the AdaLIN encoder (AdaLINE) is proposed to automatically generate normalized affine parameters. In addition, a cycle semantic consistency loss function with a single generator is proposed to ensure that the water region of the generated images remains unchanged. Finally, a two‐stage method for detecting navigable areas is proposed. In the first stage, the RRGAN is used to remove the interference on the water surface environment. In the second stage, the semantic segmentation network is used to segment the water body from the denoised image to determine the navigable areas on the water surface. The experimental results demonstrate that, in the complex and varied narrow river environment, the suggested RRGAN method can significantly reduce the reflection interference of the water surface and improve the accuracy of the water segmentation after the reflection is removed.

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A deep journey into image enhancement: A survey of current and emerging trends

Cited by 21 publications

References 232 publications

Advanced Fuzzy Sets and Genetic Algorithm Optimizer for Mammographic Image Enhancement

Advanced Fuzzy Sets and Genetic Algorithm Optimizer for Mammographic Image Enhancement

Research on image enhancement algorithm for the monitoring system in coal mine hoist

A method for detecting navigable areas in narrow rivers under complex reflection conditions

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