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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 17

An image enhancement approach based on total variation denoising and high-pass filtering with low-runtime

Toplam varyasyon tabanlı gürültü giderme ve yüksek geçiren filtrelemeye dayalı düşük işlem süresine sahip bir görüntü iyileştirme yaklaşımı

Kubilay Küçük(GAZİ ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0009-0008-2563-4225
Derya Yılmaz(GAZİ ÜNİVERSİTESİ)0000-0002-1903-7132
Pages
—
Publication Date
2026-07-14
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Experimental results demonstrate that the proposed approach improves visual quality and structural similarity index (SSIM) and additionally achieves substantial runtime advantage.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The increasing demand for high-quality visual content necessitates the development of image enhancement techniques which can effectively mitigate noise while preserving essential details.
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Key Findings & Data:In response to these challenges, an approach to image enhancement that integrates total variation denoising (TVD) with a high-pass filter (HPF) is presented.
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Conclusion & Impact:Experimental results demonstrate that the proposed approach improves visual quality and structural similarity index (SSIM) and additionally achieves substantial runtime advantage.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Görüntü keskinleştirme · Toplam değişim gürültü gidermesi · Kenar koruma

Abstract

The increasing demand for high-quality visual content necessitates the development of image enhancement techniques which can effectively mitigate noise while preserving essential details. Traditional methods often struggle to balance noise elimination and detail protection. This challenge is further compounded when computational resources are limited, making it imperative to devise solutions that are both efficient and effective. In response to these challenges, an approach to image enhancement that integrates total variation denoising (TVD) with a high-pass filter (HPF) is presented. The proposed method leverages the edge-preserving capabilities of TVD to reduce noise while maintaining structural integrity, followed by a HPF to enhance fine details and sharpen the image. To emulate realistic camera-induced degradations, a Gaussian blur noise model, which simulates loss of detail caused by flicker, zoom fluctuations, and minor handshake is employed. Experimental results demonstrate that the proposed approach improves visual quality and structural similarity index (SSIM) and additionally achieves substantial runtime advantage. The proposed framework reaches a remarkably good real-time performance of 60 frames per second (FPS) on standard CPU hardware, thereby demonstrating its suitability for time-critical and resource-constrained applications.

Özet

Yüksek kaliteli görsel içeriğe olan artan talep, temel ayrıntıları korurken gürültüyü etkili bir şekilde azaltabilen görüntü iyileştirme tekniklerinin geliştirilmesini zorunlu kılmaktadır. Geleneksel yöntemler genellikle gürültü giderme ve ayrıntı koruma arasında denge kurmakta zorlanır. Bu zorluk, hesaplama kaynakları sınırlı olduğunda daha da artarak verimli ve etkili çözümler geliştirmeyi zorunlu hale getirir. Bu zorluklara yanıt olarak, toplam değişim gürültü giderme (TVD) ve yüksek geçiren filtreyi (HPF) birleştiren bir görüntü iyileştirme yaklaşımı sunulmaktadır. Önerilen yöntem, yapısal bütünlüğü korurken gürültüyü azaltmak için TVD’nin kenar koruma yeteneklerinden yararlanır ve ardından ince ayrıntıları iyileştirmek ve görüntüyü keskinleştirmek için bir HPF kullanır. Gerçekçi kamera kaynaklı bozulmaları taklit etmek için, titreme, yakınlaştırma dalgalanmaları ve küçük el titremelerinden kaynaklanan ayrıntı kaybını simüle eden bir Gauss bulanıklığı gürültü modeli kullanılmıştır. Deneysel sonuçlar, önerilen yaklaşımın görsel kaliteyi ve yapısal benzerlik indeksini (SSIM) iyileştirdiğini ve ayrıca önemli çalışma süresi avantajı sağladığını göstermektedir. Önerilen çerçeve, standart CPU donanımında saniyede 60 kare (FPS) gibi oldukça iyi bir gerçek zamanlı performans elde ederek, zaman açısından kritik ve kaynak kısıtlı uygulamalar için uygunluğunu göstermektedir.

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Cite This Article

Küçük, K., Yılmaz, D. (2026). An image enhancement approach based on total variation denoising and high-pass filtering with low-runtime. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 17. https://doi.org/10.28948/ngumuh.1836401

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