Edge Boosted Global Awared Low-light Image Enhancement Network

Abstract

Low-light images are captured in situations where the lighting is poor or the camera hardware is not capable of producing good quality images. These types of images tend to have low contrast, blurry details, noise, and color distortion. In computer vision applications, image brightness plays a crucial role, and therefore, low-light image enhancement is used as a preprocessing step. In this study, we have improved the Low-Light Enhancement Network with Global Awareness (GLADNet) method by adding a UNet-based edge information extraction unit. The channel attention mechanism was also incorporated into the edge information extraction unit to achieve color preservation. Our experiments show that our proposed method has achieved higher PSNR, SSIM, and FSIM metrics compared to reference images. Additionally, it has produced lower NIQE and BRISQUE values for non-reference performance evaluation. Moreover, our proposed method removes noise better and produces visual results that are closer to the target images.

Keywords

• Low-light Image Enhancement
• U-net Based Edge Extraction
• Channel Attention
• Color Preservation
• Improved GladNET
• Canny

Kenar Destekli Global Farkındalı Düşük Aydınlıklı Görüntü İyileştirme Ağı

Öz

Düşük aydınlıklı görüntüler, ortam aydınlığının zayıf olduğu veya kamera donanımının iyi kalitede görüntüler üretemediği durumlarda yakalanır. Bu tür görüntüler düşük kontrast, bulanık ayrıntılar, gürültü ve renk bozulmasına sahip olma eğilimindedir. Bilgisayarlı görü uygulamalarında, görüntü parlaklığı çok önemli bir rol oynar ve bu nedenle, düşük ışıklı görüntü iyileştirme bir ön işleme adımı olarak kullanılır. Bu çalışmada, Küresel Farkındalık ile Düşük Işık İyileştirme Ağı (GLADNet) yöntemini UNet tabanlı bir kenar bilgisi çıkarma birimi ekleyerek geliştirdik. Renk korumasını sağlamak için de kanal dikkat mekanizması kenar bilgisi çıkarma birimine dahil ettik. Deneylerimiz, önerilen yöntemimizin referans görüntülere karşılaştırıldığında daha yüksek PSNR, SSIM ve FSIM metriklerine ulaştığını göstermektedir. Ayrıca, referans olmayan performans değerlendirmelerinde daha düşük NIQE ve BRISQUE değerlerine ulaşılmıştır. Önerdiğimiz yöntemin gürültüyü daha iyi gidermede ve hedef görüntülere daha yakın görsel sonuçlar ürettiği görülmüştür.

Anahtar Kelimeler

• Düşük Aydınlıklı Görüntü İyileştirme
• U-net Tabanlı Kenar Çıkarımı
• Kanal Dikkat
• Renk Korunumu
• Geliştirilmiş GladNET
• Canny

Kaynakça

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