Fuzzy fusion based high dynamic range imaging using adaptive histogram separation

Çelebi, Anıl; Duvar, Ramazan; Urhan, Oğuzhan

doi:10.1109/tce.2015.7064119

Cited by 27 publications

(27 citation statements)

References 20 publications

(46 reference statements)

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“…High Dynamic Range Extensions are known to enable optical scanners to measure such surfaces [16]. There are several different techniques used to extend the dynamic range of image sensors used in various different imaging fields [23][24][25]. In this work, multiexposure imaging, also referred to as exposure bracketing technique, is used that utilizes a set of images of the same region captured at multiple exposure times.…”

Section: High Dynamic Range Extension (Hdre)mentioning

confidence: 99%

High Precision Laser Scanning of Metallic Surfaces

Amir

Thörnberg

2017

International Journal of Optics

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Speckle noise, dynamic range of light intensity, and spurious reflections are major challenges when laser scanners are used for 3D surface acquisition. In this work, a series of image processing operations, that is, Spatial Compound Imaging, High Dynamic Range Extension, Gray Level Transformation, and Most Similar Nearest Neighbor are proposed to overcome the challenges coming from the target surface. A prototype scanner for metallic surfaces is designed to explore combinations of these image processing operations. The main goal is to find the combination of operations that will lead to the highest possible robustness and measurement precision at the lowest possible computational load. Inspection of metallic tools where the surface of its edge must be measured at micrometer precision is our test case. Precision of heights measured without using the proposed image processing is firstly analyzed to be ±7.6 m at 68% confidence level. The best achieved height precision was ±4.2 m. This improvement comes at 24 times longer processing time and five times longer scanning time. Dynamic range extension of the image capture improves robustness since the numbers of saturated or underexposed pixels are substantially reduced. Using a high dynamic range (HDR) camera offers a compromise between processing time, robustness, and precision.

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Section: High Dynamic Range Extension (Hdre)mentioning

confidence: 99%

High Precision Laser Scanning of Metallic Surfaces

Amir

Thörnberg

2017

International Journal of Optics

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“…Four possible challenging problems in single image brightening are: 1) noise in under-exposed regions could be amplified; 2) the highlight regions could be washed out; 3) there could be lightness There are two types of single image brightening algorithms. One is model-driven image processing technologies [5], [6], [7], [8], [9], [10], [11], [12] and the other is data-driven methods such as deep learning ones [13], [15]. Inputs to a model-driven image brightening algorithm are an image (images) to be processed and the related visual prior(s) [1].…”

Section: Introductionmentioning

confidence: 99%

Single Image Brightening via Multi-Scale Exposure Fusion With Hybrid Learning

Zheng

Yang

et al. 2021

IEEE Trans. Circuits Syst. Video Technol.

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A small ISO and a small exposure time are usually used to capture an image in back-or low-light condition which results in an image with negligible motion blur and small noise but looks dark. In this paper, a single image brightening algorithm is introduced to brighten such an image. The proposed algorithm includes a unique hybrid learning framework to generate two virtual images with large exposure times. The virtual images are first generated via intensity mapping functions (IMFs) which are computed using camera response functions (CRFs) and this is a model-driven approach. Both the virtual images are then enhanced by using a data-driven approach, i.e. a residual convolutional neural network to approach the ground truth images. The model-driven approach and the data-driven one compensate each other in the proposed hybrid learning framework. The final brightened image is obtained by fusing the original image and two virtual images via a multi-scale exposure fusion algorithm with properly defined weights. Experimental results show that the proposed brightening algorithm outperforms existing algorithms in terms of MEF-SSIM metric.

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“…Bu sebeple çalışmamızda özellikle bu problemin üstesinden gelmek için [8] nolu çalışma gibi tek bir imge kullanarak HDR imge oluşturmak hedeflenmiş ama bu sırada uyarlamalı bir ağırlık faktörü kullanarak geniş aralıkta tüm imgeler üzerinde iyi çalışan bir yöntem üzerinde durulmuştur. Ayrıca bunun yanında [8]'deki yöntemden farklı olarak [9] ve [12]'dekine benzer şekilde az ve çok pozlanmış imgeleri oluştururken Karşıtlık Limitli Adaptif Histogram Eşitlemesini (KLAHE) kullanmış ve böylece imgede yerel detaylar özellikle de karanlık parlak bölgelerde daha iyi bir şekilde ortaya çıkarılmıştır. [9]'daki çalışma da ise bu bildirinin genişletilmiş hali sunulmaktadır.…”

Section: Introductionunclassified

“…Ayrıca bunun yanında [8]'deki yöntemden farklı olarak [9] ve [12]'dekine benzer şekilde az ve çok pozlanmış imgeleri oluştururken Karşıtlık Limitli Adaptif Histogram Eşitlemesini (KLAHE) kullanmış ve böylece imgede yerel detaylar özellikle de karanlık parlak bölgelerde daha iyi bir şekilde ortaya çıkarılmıştır. [9]'daki çalışma da ise bu bildirinin genişletilmiş hali sunulmaktadır. [9]'daki çalışmada LDR imgelerin birleştirilmesi sırasında bulanık mantık temelli bir yaklaşım kullanılmış bu şekilde görsel olarak daha iyi başarı elde edilmiş ancak işlem süresi bir miktar artmıştır.…”

Section: Introductionunclassified

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High dynamic range imaging on a mobile device using single input image

Çelebi

Duvar

Urhan

2015

2015 23nd Signal Processing and Communications Applications Conference (SIU)

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Özetçe-Bu çalışmada tek bir giriş imgesi kullanarak mobil bir cihaz üzerinde çalışabilecek yüksek dinamik aralığa sahip imge elde etmeye olanak sağlayan bir yöntem sunulmaktadır. Bu yaklaşım HDR imge oluştururken tek bir imge kullandığı için çoklu imge kullanıldığı zamanlarda kamera hareketinden veya hareketli nesnelerden kaynaklanan hayalet etkisi problemini bulundurmamaktadır. Kullanılan uyarlamalı histogram ayırma yaklaşımı farklı karakteristiğe sahip tek bir imgeden verimli bir şekilde iki tane düşük dinamik aralığa sahip imge elde etmemizi sağlamaktadır. Kullanılan yaklaşım oldukça düşük işlem yüküne sahiptir. Bu sebebten akıllı telefonlar ve kompakt kamerelar gibi mobil cihazlara uygulanması mümkündür. Deneysel sonuçlar kısmında kullanılan yaklaşımın karşılaştırılan diğer yöntemlere göre daha başarılı sonuçlar verdiği gösterilmektedir. Yöntem Samsung Galaxy Note 3 model akıllı telefon üzerinde JNI (Java Native Interface) kullanılarak verimli şekilde çalıştırılmıştır. Anahtar Kelimeler -Hdr imge, Histogram ayırmaAbstract-In this work, we present a high dynamic range (HDR) image generation method using a single input image that can be executed on a mobile device. The utilized method eliminates ghosting effect inherently occurring in HDR images when several input image containing camera/object motion are employed. The employed adaptive histogram separation scheme enables efficient low dynamic range image generation from a single input image with different characteristic and hence provides improved HDR performance. Since the utilized approach is computationally lightweight, it is possible to implement it on mobile devices such as smart phones and compact cameras. It is shown that the empolyed approach provides ghost-free and improved HDR performance compared to existing methods at experimental results. The HDR method is implemented on Samsung Galaxy Note 3 smart phone using Java Native Interface (JNI) efficiently.

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Fuzzy fusion based high dynamic range imaging using adaptive histogram separation

Cited by 27 publications

References 20 publications

High Precision Laser Scanning of Metallic Surfaces

High Precision Laser Scanning of Metallic Surfaces

Single Image Brightening via Multi-Scale Exposure Fusion With Hybrid Learning

High dynamic range imaging on a mobile device using single input image

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