An Effective Image Contrast Enhancement Method Using Global Histogram Equalization

Yousuf, Muhammad; Rakib, Md. Rashadul

doi:10.3329/jsr.v3i1.5299

Cited by 10 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, five popular image enhancement methods are applied, including linear transformation [22], histogram normalization [23], gamma transform [24], global histogram equalization [25], and contrast limited adaptive histogram equalization (CLAHE) [26]. The experiment's concept is as follows: the water target image is enhanced and then detected using the pruned network algorithm, and the results are compared to the original image without preprocessing, in order to investigate the effect of image enhancement on water target recognition in various environments.…”

Section: Preprocessing Based On Special Weathermentioning

confidence: 99%

Water Target Recognition Method and Application for Unmanned Surface Vessels

et al. 2022

View full text Add to dashboard Cite

Water target recognition is a critical challenge for the perception technology of unmanned surface vessels (USVs). In the application of USV, detection accuracy and the inference time both matter, while it is tough to strike a balance and single-frame water target detection behaves unstable in the video detection. To solve these problems, many strategies are applied to increase YOLOv4's performance, including network pruning, the focal loss function, blank label training, and preprocessing with histogram normalization. The optimized detection method achieves a mean average precision (mAP) of 81.74% and a prediction speed of 26.77 frames per second (FPS), which meets the USV navigation requirements. To build the integrated USV-based system for water target recognition, a water target dataset containing 9936 images is created from offshore USV experiments in which the human-in-the-loop annotation and mosaic data augmentation methods are used. The issues of miss detection and false alarm can be considerably mitigated by cascading the Siamese-RPN tracking network, and the major color of a water target can be retrieved using a local contrast saliency color detection scheme. The system being tested is called "ME120" includes an embedded edge computing platform (Nvidia Jetson AGX Xavier). Finally, online dataset learning demonstrates the improved YOLOv4 achieves an increase of 66.98% in FPS at the cost of a decrease of 0.79% in mAP when compared with the original YOLOv4 and offline navigation experiments validate that our system achieves high recognition capability while maintaining a high degree of robustness.

show abstract

Section: Preprocessing Based On Special Weathermentioning

confidence: 99%

Water Target Recognition Method and Application for Unmanned Surface Vessels

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Deblurring, filtering, and sharpening image features like edges, boundaries, or contrast to make the image suitable for better analysis and enhancing the luminance component, which only increases the rightness of the image, were the foundations of previous works of enhancement techniques. Histogram Equalization (HE) [11], Local Histogram Equalization (LHE) [12], Contrast-Limited Adaptive Histogram Equalization (CLAHE), and Global Histogram Equalization (GHE) [13][14][15][16] are some of the most common traditional gray image enhancement techniques. However, the main drawbacks of these methods are unsightly visual artifacts like over enhancement, level saturation, and increased noise level [17,18].…”

Section: Introductionmentioning

confidence: 99%

A novel medical image enhancement technique based on hybrid method

Khudhair

Najjar

Waheed

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Medical images are a specific type of image that can be used to diagnose disease in patients. Critical uses for medical images can be found in many different areas of medicine and healthcare technology. Generally, the medical images produced by these imaging methods have low contrast. As a result, such types of images need immediate and fast enhancement. This paper introduced a novel image enhancement methodology based on the Laplacian filter, contrast limited adaptive histogram equalization, and an adjustment algorithm. Two image datasets were used to test the proposed method: The DRIVE dataset, forty images from the COVID-19 Radiography Database, endometrioma-11, normal-brain-MRI-6, and simple-breast-cyst-2. In addition, we used the robust MATLAB package to evaluate our proposed algorithm’s efficacy. The results are compared quantitatively, and their efficacy is assessed using four metrics: Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Contrast to Noise Ratio (CNR), and Entropy (Ent). The experiments show that the proposed method yields improved images of higher quality than those obtained from state-of-the-art techniques regarding MSE, CNR, PSNR, and Ent metrics.

show abstract

“…It is important to highlight the recent studies that tried to improve the low‐contrast of CT images. Georgieva () used a gamma correction procedure to process a selected region of interest (ROI) of a given image, while Yousuf and Rakib() proposed a global histogram equalization technique based on a probability function computed from a defined image ROI. Likewise, Ismail and Sim (2011) introduced a dynamic histogram equalization technique, which maintains the mean brightness of the inputted image to produce decent results, while Tan et al .…”

Section: Introductionmentioning

confidence: 99%

A new algorithm for improving the low contrast of computed tomography images using tuned brightness controlled single-scale Retinex

Al-Ameen

Sulong

2015

Scanning

View full text Add to dashboard Cite

Contrast is a distinctive visual attribute that indicates the quality of an image. Computed Tomography (CT) images are often characterized as poor quality due to their low-contrast nature. Although many innovative ideas have been proposed to overcome this problem, the outcomes, especially in terms of accuracy, visual quality and speed, are falling short and there remains considerable room for improvement. Therefore, an improved version of the single-scale Retinex algorithm is proposed to enhance the contrast while preserving the standard brightness and natural appearance, with low implementation time and without accentuating the noise for CT images. The novelties of the proposed algorithm consist of tuning the standard single-scale Retinex, adding a normalized-ameliorated Sigmoid function and adapting some parameters to improve its enhancement ability. The proposed algorithm is tested with synthetically and naturally degraded low-contrast CT images, and its performance is also verified with contemporary enhancement techniques using two prevalent quality evaluation metrics-SSIM and UIQI. The results obtained from intensive experiments exhibited significant improvement not only in enhancing the contrast but also in increasing the visual quality of the processed images. Finally, the proposed low-complexity algorithm provided satisfactory results with no apparent errors and outperformed all the comparative methods.

show abstract

An Effective Image Contrast Enhancement Method Using Global Histogram Equalization

Cited by 10 publications

References 18 publications

Water Target Recognition Method and Application for Unmanned Surface Vessels

Water Target Recognition Method and Application for Unmanned Surface Vessels

A novel medical image enhancement technique based on hybrid method

A new algorithm for improving the low contrast of computed tomography images using tuned brightness controlled single-scale Retinex

Contact Info

Product

Resources

About