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Abstract

Multiple exposure fusion (MEF) is attracting considerable attention in research on high dynamic range (HDR) imaging: Eliminating the need to generate an intermediate HDR image, MEF directly expands an image’s dynamic range and thus provides greater detail enhancement than traditional HDR techniques. However, in the fusion stage, the optimal weights of each pixel in the images input to the final synthesized image are challenging to determine and usually required manual tuning of parameters. In addition, many MEF algorithms have been proposed, but most have lacked a self-regulation mechanism. To tackle the above disadvantages, we apply fuzzy theory and present a novel MEF framework with a fuzzy feedback structure. In this work, over- and under-exposed images are generated from a single input image using local histogram stretching. This avoids the creation of ghost artifacts when multiple exposed images are fused in the dynamic scene containing object motion. In the fusion stage, fuzzy logic is used to determine pixel weights based on gradient and chrominance analysis, and a guided image filter is used to suppress noise and enhance edges in the weight maps. To ensure detail enhancement without excessive or insufficient sharpness, we developed a simple sharpness measure named the edge-map overlapping rate (EOR). With EOR and the feedback structure, users are allowed to manipulate the output synthesized image to their preferred sharpness level, and the above weights are appropriately redesigned by automatically regulating the magnitude of the fuzzy input. From experimental results, this work demonstrated excellent image quality and outperformed other existing HDR/MEF methods.

Keywords

Fuzzy logic fuzzy feedback high dynamic range (HDR)multiple exposure fusion (MEF)

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References

P.E.

Debevec and

Malik , Recovering high dynamic range radiance maps from photographs, Proc SIGGRAPH, Los Angeles, CA, USA, 1997, pp. 369–378.

Z.G.

Li and

J.H.

Zheng , Visual-salience-based tone mapping for high dynamic range images, IEEE Trans Ind Electron 61(12) (2014), 7076–7082.

Shen ,

Zhao ,

Jiang and

Suwa , Recovering high dynamic range by multi-exposure Retinex, J Vis Commun Image Represent 20(8) (2009), 521–531.

Gu ,

Li ,

Wong ,

Zhu and

Wang , Gradient field multi-exposure images fusion for high dynamic range image visualization, J Vis Commun Image Represent 23(4) (2012), 604–610.

Shen ,

Zhao ,

Yan and

Li , Exposure fusion using boosting Laplacian pyramid, IEEE Trans Cybern 44(9) (2014), 1579–1590.

Patel ,

Androutsos and

Kyan , Adaptive exposure fusion for high dynamic range imaging, Proc IEEE Int Conf Image Process (ICIP), Quebec, Canada, 2015, pp. 4679–4683.

Z.G.

Li ,

Wei ,

Wen and

Zheng , Detail-enhanced multi-scale exposure fusion, IEEE Trans Image Process 26(3) (2017), 1243–1252.

Hua ,

Wang ,

Rusdi and

Jiang , A novel multi-focus image fusion algorithm based on random walks, J Visual Communication and Image Representation 25(5) (2014), 951–962.

Ma and

Wang , Multi-exposure image fusion: A patch-wise approach, Proc IEEE Int Conf Image Process (ICIP), Quebec, Canada, 2015, pp. 1717–1721.

10.

Hua ,

Wang ,

Yeh ,

Cheng and

Lai , Background extraction using random walk image fusion, IEEE Trans Cybernetics 99 (2016).

11.

Sanchez-Riera ,

Hua ,

Hsiao ,

Lim ,

Hidayati and

Cheng , A comparative study of data fusion for RGB- D based visual recognition, Pattern Recognition Letters 73 (2016), 1–6.

12.

Li ,

Zheng ,

Zhu ,

Yao ,

Wu and

Rahardja , Hybrid patching for a sequence of differently exposed images with moving objects, IEEE Trans Image Process 22(12) (2013), 5190–5201.

13.

Li ,

Zheng ,

Zhu and

Wu , Selectively detail-enhanced fusion of differently exposed images with moving objects, IEEE Trans Image Process 23(10) (2014), 4372–4382.

14.

Bhoskar and

Kakarala , Novel approach to detect HDR scenes and determine suitable frames for image fusion, Proc Digital Photography and Mobile Imaging XII, San Francisco, CA, USA, 2016, pp. 1–8.

15.

Ha ,

Yuan ,

Fierro and

Ha , Ghost removal in exposure fusion by temporal consistency assessment, J Imaging Science and Technology (JIST) 60(4) (2016), 40501:1–40501:12.

16.

Zhang ,

Hu ,

Liu and

Yao , Motion-free exposure fusion based on inter-consistency and intra-consistency, Information Sciences 376 (2017), 190–201.

17.

Im ,

Jeon ,

Hayes and

Paik , Single image-based ghost-free high dynamic range imaging using local histogram stretching and spatially-adaptive denoising, IEEE Trans Image Process 57(4) (2011), 1478–1484.

18.

Celebi ,

Duvar and

Urhan , Fuzzy fusion based high dynamic range imaging using adaptive histogram separation, IEEE Trans Consumer Electronics 61(1) (2015), 119–127.

19.

Raveendranath and

A.K.

Johnson , Single shot high dynamic range imaging using power law transformation and exposure fusion, Proc Int Conf Communication Systems and Networks (ComNet), Thiruvananthapuram, India, 2016, pp. 120–126.

20.

Gu ,

Li ,

Zhu and

Wang , Local edge-preserving multiscale decomposition for HDR image tone mapping, IEEE Trans Image Process 22(1) (2013), 70–79.

21.

Song ,

Wang and

Bai , High dynamic range infrared images detail enhancement based on local edge preserving filter, Infrared Physics & Technology 77 (2016), 464–473.

22.

Kim ,

Park and

Chang , Tone mapping with contrast preservation and lightness correction in high dynamic range imaging, Signal, Image and Video Processing 10(8) (2016), 1425–1432.

23.

Ok and

Lee , HDR tone mapping algorithm based on difference compression with adaptive reference values, J Visual Commun and Image Represent 43 (2017), 61–76.

24.

He ,

Sun and

Tang , Guided image filtering, IEEE Trans Pattern Analysis and Machine Intelligence 35(6) (2013), 1397–1409.

25.

He and

Sun , Fast guided filter, Tech Report (2015).

26.

M.D.

Fairchild , The HDR photographic survey, Proc 15th IS&T/SID Color Imaging Conference, Albuquerque, NM, USA, vol. 15, 2006, pp. 233–1238.

27.

N.D.

Narvekar and

L.J.

Karam , A no-reference image blur metric based on the cumulative probability of blur detection (CPBD), IEEE Trans Image Process 20(9) (2011), 2678–2683.

Multiple exposure fusion based on sharpness-controllable fuzzy feedback

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