Low dynamic range histogram equalization (LDR-HE) via quantized Haar wavelet transform

Bulut, Faruk

doi:10.1007/s00371-021-02281-5

Cited by 24 publications

(13 citation statements)

References 36 publications

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“…At this point, it will be better to emphasize that standard HE methods might produce under enhancement or over enhancement rates by producing visual artifacts 28 . An adaptive HE method normally can produce better results.…”

Section: Methodsmentioning

confidence: 99%

“…At this point, it will be better to emphasize that standard HE methods might produce under enhancement or over enhancement rates by producing visual artifacts. 28 An adaptive HE method normally can produce better results. For this study, a recently presented contrast enhancement method, low dynamic range HE, 28 might produce better results.…”

Section: Uhfimentioning

confidence: 99%

“…28 An adaptive HE method normally can produce better results. For this study, a recently presented contrast enhancement method, low dynamic range HE, 28 might produce better results. Because the motivation in this study is to present a remarkable blueprint in iris recognition, these kinds of fine-tuning procedures might be implemented in future studies.…”

Section: Uhfimentioning

confidence: 99%

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Performance boosting of image matching-based iris recognition systems using deformable circular hollow kernels and uniform histogram fusion images

2022

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. Identification of people using different biometric data is becoming more important in network society. Biometrics include voice, ears, palms, fingerprints, faces, iris, retina, and hand shapes. Among these features, iris detection gets more attention because each iris type is unique and does not change throughout life. In this study, an iris recognition framework is proposed using deformable circular hollow kernels and uniform histogram fusion images (UHFIs). This system introduces two different approaches for the iris recognition: one with image matching without machine learning and the other one with machine learning. In the first approach, image matching through Gabor features is employed. After getting the circularly cropped UHFI, Gabor features are extracted and employed in the image matching-based recognition system. In addition, a normalized cross-correlation coefficient is used as a similarity metric to compare the Gabor feature vectors. In the second approach, Gabor feature images are extracted and employed in deep learning (DL). According to the experimental results, the proposed system reaches around 89% accuracy on MMU1, 86% accuracy on MMU2, and about 50% accuracy on the CASIAV3 and CASIAV4 datasets when no machine learning is employed. Extensive benchmarking results also indicate that the proposed system boosts the performance of conventional systems by at least around 40% in terms of accuracy in the absence of machine learning. However, in the presence of machine learning, experimental results show that the proposed method with DL achieves an accuracy of up to 100% for MMU1, MMU2, CASIAV3, and CASIAV4 datasets.

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Section: Methodsmentioning

confidence: 99%

Section: Uhfimentioning

confidence: 99%

Section: Uhfimentioning

confidence: 99%

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Performance boosting of image matching-based iris recognition systems using deformable circular hollow kernels and uniform histogram fusion images

2022

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“…Most of these CE methods have been investigated by visual inspection and image quality metrics such as AMBE, 34 DE, 7 EME, 36 QRCM, 37 UIQ, 38 CII, 27 TMQI. 39 A more detailed overview of further image quality metrics and methods for contrast enhancement can be found here.…”

Section: Introductionmentioning

confidence: 99%

Comparison of algorithms for contrast enhancement based on TOD assessments by convolutional neural networks

Wegner

Keßler

2022

Electro-Optical and Infrared Systems: Technology and Applications XIX

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A current approach for performance assessment of imagers is triangle orientation discrimination (TOD). This approach requires observers or human visual system (HVS) models to recognize equilateral triangles pointing in four different directions. Imagers may apply embedded advanced digital signal processing (ADSP) for contrast enhancement, noise reduction, edge sharpening, etc. Unfortunately, applied methods are in general not documented and hence unknown. Within the last decades a vast amount of techniques for contrast enhancement has been proposed. There are some comparisons of such algorithms for few images and figures of merit. However, many of these figures of merit cannot assess usability of altered image content for specific tasks such as object recognition In this work different algorithms for contrast enhancement are compared in terms of TOD assessments by convolutional neural networks (CNN) as models. These models are trained by artificial images with single triangles. Many methods for contrast enhancement highly depend on the content of the entire image. Therefore, the images are superimposed by natural backgrounds with varying standard deviations to provide different signal-to-background ratios. Then these images are degraded by Gaussian blur and noise representing degradational camera effects and sensor noise. Different algorithms are applied, such as the contrast-limited adaptive histogram equalization or local range modification. Then accuracies of the trained models on these images are compared for different ADSP algorithms. Accuracy gains for low signal-to-background ratio and sufficiently large triangles are found, while impairments are found for high signal-to-background ratio and small triangles. Finally, implications of replacing triangles by real target signatures when using such ADSP algorithms are discussed. The results can be a step towards the assessment of those algorithms for generic target recognition.

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“…Bu yaklaşımlar genel olarak üretilen çıktılarda girdi görüntüsüne ait olan parlaklığını korumasına karşın, doğal görünümlü görüntüler üretmede başarısız olabilirler. Bu alanda yapılmış olan bir başka çalışmada Haar dalgacık dönüşümü kullanılarak ileri düzey bir histogram eşitleme yöntemi önerilmiştir (Bulut, 2021). Öncelikle RGB kanalındaki bilgiler frekansa düzleminde ele alınmış ve ortalama renk bilgisi ile kıyaslanarak daha ileri düzey bir baskılama işlemi yapılmıştır.…”

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A New Histogram Equalization Method With Modified Discrete Haar Wavelet Transform

Bulut¹

2022

Mühendislik Bilimleri Ve Tasarım Dergisi

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Histogram eşitleme dijital görüntülerin kontrastını artırmak için kullanılan yöntemlerden biridir. İdeal histogram eşitleme yöntemlerinde, girdi ve çıktı arasındaki görüntü benzerliğini koruyarak histogramdaki dağılımları tekdüze hale getirerek kontrast gerilmektedir. Frekans alanında yapılan bu çalışmada, Düşük Dinamik Aralığında değiştirilmiş ayrık Haar Dalgacık Dönüşümü ile yeni bir görüntü kontrast germe yöntemi önerilmiştir. Bu yöntemde Olasılık Kütle Fonksiyonunu ile frekansların yüksek geçiş kanalında gürültülü frekanslara bir baskılama işlemi gerçekleştirilmiştir. Daha sonra yapılan frekans dönüşümlerinde histogram frekansların dinamik aralıklarında önemli bir azalma sağlanmıştır. Frekans alanındaki bu işlem görüntüde standart sapmanın artmasını sağlayarak görüntü kalitesinin iyileşmesini sağlar. Kıyaslamalı bir veri seti üzerinde yapılan deneysel çalışmalarda, önerilen yöntem konvansiyonel metotlarla kıyaslanmış ve umut verici sonuçlar elde edilmiştir. Görüntü kalitesi değerlendirme metriklerinden Tepe Sinyal Gürültü Oranı (PSNR), Ortalama Kare Hata (MSE), Yapısal Benzerlik Endeks Ölçütü (SSIM) ve Kontrast değeri deneysel çalışmalarda kullanılmıştır. Önerilen bu yaklaşım ile elde edilen sonuçlar diğer algoritmaların sonuçları ile kıyaslandığında hem kalitatif hem de kantitatif açıdan başarılı bulunmuştur.

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Low dynamic range histogram equalization (LDR-HE) via quantized Haar wavelet transform

Cited by 24 publications

References 36 publications

Performance boosting of image matching-based iris recognition systems using deformable circular hollow kernels and uniform histogram fusion images

Performance boosting of image matching-based iris recognition systems using deformable circular hollow kernels and uniform histogram fusion images

Comparison of algorithms for contrast enhancement based on TOD assessments by convolutional neural networks

A New Histogram Equalization Method With Modified Discrete Haar Wavelet Transform

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