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Advanced Histogram Equalization Based on a Hybrid Saliency Map and Novel Visual Prior

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Abstract

The traditional grayscale histogram of an input image is constructed by simply counting its pixels. Hence, the classical histogram equalization (HE) technique has fundamental defects such as overenhancement, underenhancement, and brightness drifting. This paper proposes an advanced HE based on a hybrid saliency map and a novel visual prior to addressing the defects mentioned above. First, the texture saliency map and attention weight map are constructed based on the texture saliency and visual attention mechanism. Later, the hybrid saliency map that is obtained by fusing the texture and attention weight maps is used to derive the saliency histogram. Then, a novel visual prior, the narrow dynamic range prior (NDP), is proposed, and the saliency histogram is modified by calculating the optimal parameter in combination with a binary optimization model. Next, the cumulative distribution function (CDF) is rectified to control the brightness. Finally, the hybrid saliency map is applied again for local enhancement. Compared with several state-of-the-art algorithms qualitatively and quantitatively, the proposed algorithm effectively improves the contrast of the image, generates better subjective visual perception, and presents better performance broadly.

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Authors and Affiliations

  1. School of Information Science and Engineering, Huaqiao University, Xiamen, 361021, China

    Yuanbin Wu & Shengkui Dai

  2. School of Electronic Science and Engineering, Nanjing University, Nanjing, 210093, China

    Zhan Ma

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Correspondence to Shengkui Dai.

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Additional information

Yuanbin Wu received the B. Sc. degree in communication engineering from Huaqiao University, China in 2020. He is currently a master student in transportation at Huaqiao University, China.

His research interests include image/video processing and computer vision.

Shengkui Dai received the B. Sc. degree in electronic engineering from South-Central University for Nationalities, China in 1993, the M. Sc. degree in pattern recognition and intelligent systems and Ph. D. degree in information and communication engineering from Huazhong University of Science and Technology, China in 2001 and 2005, respectively. From 2005 to 2007, he was engaged in post-doctoral research at Huazhong University of Science and Technology, China. He is currently with School of Information Science and Engineering, Huaqiao University, China.

His research interests include image video processing and computer vision.

Zhan Ma received the B. Sc. and M. Sc. degrees in electrical engineering from Huazhong University of Science and Technology, China in 2004 and 2006, respectively, and the Ph. D. degree in electrical engineering from New York University, USA in 2011. From 2011 to 2014, he was with Samsung Research America, USA, and Futurewei Technologies, Inc., USA. He is currently with School of Electronic Science and Engineering, Nanjing University, China. He was a co-recipient of the 2018 PCM Best Paper Finalist, the 2019 IEEE Broadcast Technology Society Best Paper Award, and the 2020 IEEE MMSP Grand Challenge Best Image Coding Solution. He is a senior member of IEEE.

His research interests include learning-based image/video coding and computational imaging.

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Wu, Y., Dai, S. & Ma, Z. Advanced Histogram Equalization Based on a Hybrid Saliency Map and Novel Visual Prior. Mach. Intell. Res. 21, 1178–1191 (2024). https://doi.org/10.1007/s11633-023-1448-2

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