Multi-Scale FPGA-Based Infrared Image Enhancement by Using RGF and CLAHE

Liu, Jialong; Zhou, Xichuan; Wan, Zhenlong; Yang, Xuefei; He, Wei; He, Rulong; Lin, Yingcheng

doi:10.3390/s23198101

Cited by 7 publications

(6 citation statements)

References 49 publications

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“…Strength Limitations HE, AHE, CLAHE on CPU 13 Shows that CLAHE is the most suitable technique in general condition…”

Section: Methodsmentioning

confidence: 99%

Enhancing the Dorsal Side of Fingers Using An Image Enhancement Technique with FPGA Output Comparison

Han,

Riaz,

Ali

2024

Baghdad Sci.J

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يعرض هذا البحث عملاً على تقنية تحسين تسمى معادلة الرسم البياني، HE لصور مفصل الإصبع. وينقسم هذا المشروع إلى ثلاث مراحل، وهي الحصول على الصور، وتحسين الصورة، والتقييم. للحصول على الصور، يتم إعداد كاميرا ويب USB كجهاز الحصول على صورة الأصابع. لتحسين الصورة، يتم اختيار طريقة معادلة الرسم البياني (HE) بسبب الخوارزمية الأقل تعقيدًا، خاصة عند تقييم الأداء على منصة FPGA. تم اعتبار منصتي معالجة لإكمال الدراسة، وهما أجهزة الكمبيوتر المكتبية التي تستخدم برمجة MATLAB ومنصة FPGA DE1-SoC. تم إجراء مقارنة النتائج بين منصتي المعالجة هاتين، حيث وجد أن النتائج لكلا المنصتين أظهرت مخرجات متطابقة من حيث PSNR، والتي حققت قيمة 13.43 ديسيبل وMSE بقيمة 0.0454.

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“…Strength Limitations HE, AHE, CLAHE on CPU 13 Shows that CLAHE is the most suitable technique in general condition…”

Section: Methodsmentioning

confidence: 99%

Enhancing the Dorsal Side of Fingers Using An Image Enhancement Technique with FPGA Output Comparison

Han,

Riaz,

Ali

2024

Baghdad Sci.J

View full text Add to dashboard Cite

show abstract

“…CLAHE clips and redistributes the histograms of each sub-image, thereby limiting the degree of contrast enhancement. CLAHE prevents the amplification of noise and excessive enhancement [29]. The ULIE method initially divides the original image into several non-overlapping sub-images, each denoted as s. We compute the the frequency of pixel values p s (i), representing the data distribution of pixel values i within each sub-image.…”

Section: Uneven-light Image Enhancement For Illumination-aware Region...mentioning

confidence: 99%

ISLS: An Illumination-aware Sauce-packet Leakage Segmentation Method

You,

Lin,

Feng

et al. 2024

Preprint

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The segmentation of abnormal regions is vital in smart manufacturing. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby impeding the rapid production of industrial assembly lines. To alleviate this issue, we propose the two-stage Illumination-aware Sauce-packet Leakage Segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: Illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose an image enhancement to relieve the impact of uneven illumination, enhancing the texture details of ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the Multi-scale Feature Fusion Module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by adaptive weighting of spatial and channel dimensions. Furthermore, we generated a self-built dataset of sauce-packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method. Our code is available at https://github.com/LSJ5106/SauceDetect.

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“…CLAHE clips and redistributes the histograms of each sub-image, thereby limiting the degree of contrast enhancement. CLAHE prevents the amplification of noise and excessive enhancement [34]. The ULIE method initially divides the original image into several non-overlapping sub-images, each denoted as s. We compute the frequency of pixel values p s (i), representing the data distribution of pixel values i within each sub-image.…”

Section: Uneven-light Image Enhancement For Illumination-aware Region...mentioning

confidence: 99%

ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method

You,

Lin,

Feng

et al. 2024

Sensors

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The segmentation of abnormal regions is vital in smart manufacturing. The blurring sauce-packet leakage segmentation task (BSLST) is designed to distinguish the sauce packet and the leakage’s foreground and background at the pixel level. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby hindering the measurements of leakage area and impeding the automated sauce-packet production. To alleviate this issue, we propose the two-stage illumination-aware sauce-packet leakage segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose image enhancement to relieve the impact of uneven illumination, enhancing the texture details of the ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the multi-scale feature fusion module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by the MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by the adaptive weighting of spatial and channel dimensions. Furthermore, we generate a self-built dataset of sauce packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method.

show abstract

Multi-Scale FPGA-Based Infrared Image Enhancement by Using RGF and CLAHE

Cited by 7 publications

References 49 publications

Enhancing the Dorsal Side of Fingers Using An Image Enhancement Technique with FPGA Output Comparison

Enhancing the Dorsal Side of Fingers Using An Image Enhancement Technique with FPGA Output Comparison

ISLS: An Illumination-aware Sauce-packet Leakage Segmentation Method

ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method

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