skip to main content
10.1145/3503161.3548016acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
research-article

FMNet: Frequency-Aware Modulation Network for SDR-to-HDR Translation

Authors Info & Claims
Published:10 October 2022Publication History

ABSTRACT

High-dynamic-range (HDR) media resources that preserve high contrast and more details in shadow and highlight areas in television are becoming increasingly popular for modern display technology compared to the widely available standard-dynamic-range (SDR) media resources. However, due to the exorbitant price of HDR cameras, researchers have attempted to develop the SDR-to-HDR techniques to convert the abundant SDR media resources to the HDR versions for cost-saving. Recent SDR-to-HDR methods mostly apply the image-adaptive modulation scheme to dynamically modulate the local contrast. However, these methods often fail to properly capture the low-frequency cues, resulting in artifacts in the low-frequency regions and low visual quality. Motivated by the Discrete Cosine Transform (DCT), in this paper, we propose a Frequency-aware Modulation Network (FMNet) to enhance the contrast in a frequency-adaptive way for SDR-to-HDR translation. Specifically, we design a frequency-aware modulation block that can dynamically modulate the features according to its frequency-domain responses. This allows us to reduce the structural distortions and artifacts in the translated low-frequency regions and reconstruct high-quality HDR content in the translated results. Experimental results on the HDRTV1K dataset show that our FMNet outperforms previous methods and the perceptual quality of the generated HDR images can be largely improved. Our code is available at https://github.com/MCG-NKU/FMNet.

Skip Supplemental Material Section

Supplemental Material

MM22-fp1123.mp4

mp4

6.5 MB

References

  1. Nasir Ahmed, T_ Natarajan, and Kamisetty R Rao. 1974. Discrete cosine transform. IEEE transactions on Computers 100, 1 (1974), 90--93.Google ScholarGoogle Scholar
  2. Aman R Chadha, Pallavi P Vaidya, and M Mani Roja. 2011. Face recognition using discrete cosine transform for global and local features. In 2011 International Conference On Recent Advancements In Electrical, Electronics And Control Engineering. IEEE, 502--505.Google ScholarGoogle ScholarCross RefCross Ref
  3. Guangyao Chen, Peixi Peng, Li Ma, Jia Li, Lin Du, and Yonghong Tian. 2021. Amplitude-phase recombination: Rethinking robustness of convolutional neural networks in frequency domain. In Int. Conf. Comput. Vis. 458--467.Google ScholarGoogle ScholarCross RefCross Ref
  4. Wenlin Chen, James Wilson, Stephen Tyree, Kilian QWeinberger, and Yixin Chen. 2016. Compressing convolutional neural networks in the frequency domain. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1475--1484.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Xiangyu Chen, Zhengwen Zhang, Jimmy S. Ren, Lynhoo Tian, Yu Qiao, and Chao Dong. 2021. A New Journey from SDRTV to HDRTV. In Int. Conf. Comput. Vis.Google ScholarGoogle ScholarCross RefCross Ref
  6. Paul E. Debevec and Jitendra Malik. 2008. Recovering high dynamic range radiance maps from photographs. In ACM SIGGRAPH Anal. Conf.Google ScholarGoogle Scholar
  7. Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. 2015. Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38, 2 (2015), 295--307.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Michaël Gharbi, Jiawen Chen, Jonathan T Barron, Samuel W Hasinoff, and Frédo Durand. 2017. Deep bilateral learning for real-time image enhancement. ACM Trans. Graph. 36, 4 (2017), 1--12.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Dong Gong, Jie Yang, Lingqiao Liu, Yanning Zhang, Ian Reid, Chunhua Shen, Anton Van Den Hengel, and Qinfeng Shi. 2017. From motion blur to motion flow: A deep learning solution for removing heterogeneous motion blur. In IEEE Conf. Comput. Vis. Pattern Recog. 2319--2328.Google ScholarGoogle ScholarCross RefCross Ref
  10. Lionel Gueguen, Alex Sergeev, Ben Kadlec, Rosanne Liu, and Jason Yosinski. 2018. Faster neural networks straight from jpeg. Adv. Neural Inform. Process. Syst. 31 (2018).Google ScholarGoogle Scholar
  11. Jingwen He, Yihao Liu, Yu Qiao, and Chao Dong. 2020. Conditional sequential modulation for efficient global image retouching. In Eur. Conf. Comput. Vis. Springer, 679--695.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In IEEE Conf. Comput. Vis. Pattern Recog. 770--778.Google ScholarGoogle ScholarCross RefCross Ref
  13. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Identity mappings in deep residual networks. In Eur. Conf. Comput. Vis. Springer, 630--645.Google ScholarGoogle ScholarCross RefCross Ref
  14. Yongqing Huo, Fan Yang, Le Dong, and Vincent Brost. 2014. Physiological inverse tone mapping based on retina response. The Visual Computer 30, 5 (2014), 507--517.Google ScholarGoogle ScholarCross RefCross Ref
  15. Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Int. Conf. Mach. Learn. PMLR, 448--456.Google ScholarGoogle Scholar
  16. Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. 2017. Image-toimage translation with conditional adversarial networks. In IEEE Conf. Comput. Vis. Pattern Recog. 1125--1134.Google ScholarGoogle Scholar
  17. Soo Ye Kim and Munchurl Kim. 2018. A multi-purpose convolutional neural network for simultaneous super-resolution and high dynamic range image reconstruction. In Asian Conf. Comput. Vis. Springer, 379--394.Google ScholarGoogle Scholar
  18. Soo Ye Kim, Jihyong Oh, and Munchurl Kim. 2019. Deep sr-itm: Joint learning of super-resolution and inverse tone-mapping for 4k uhd hdr applications. In Int. Conf. Comput. Vis. 3116--3125.Google ScholarGoogle ScholarCross RefCross Ref
  19. Soo Ye Kim, Jihyong Oh, and Munchurl Kim. 2020. Jsi-gan: Gan-based joint super-resolution and inverse tone-mapping with pixel-wise task-specific filters for uhd hdr video. In Association for the Advancement of Artificial Intelligence. 11287--11295.Google ScholarGoogle Scholar
  20. Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Int. Conf. Learn. Represent.Google ScholarGoogle Scholar
  21. Rafael P Kovaleski and Manuel M Oliveira. 2014. High-quality reverse tone mapping for a wide range of exposures. In 2014 27th SIBGRAPI Conference on Graphics, Patterns and Images. IEEE, 49--56.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. 2017. Enhanced deep residual networks for single image super-resolution. In IEEE Conf. Comput. Vis. Pattern Recog. Worksh. 136--144.Google ScholarGoogle ScholarCross RefCross Ref
  23. Yu-Lun Liu, Wei-Sheng Lai, Yu-Sheng Chen, Yi-Lung Kao, Ming-Hsuan Yang, Yung-Yu Chuang, and Jia-Bin Huang. 2020. Single-image HDR reconstruction by learning to reverse the camera pipeline. In IEEE Conf. Comput. Vis. Pattern Recog. 1651--1660.Google ScholarGoogle ScholarCross RefCross Ref
  24. Zhenhua Liu, Jizheng Xu, Xiulian Peng, and Ruiqin Xiong. 2018. Frequencydomain dynamic pruning for convolutional neural networks. Adv. Neural Inform. Process. Syst. 31 (2018).Google ScholarGoogle Scholar
  25. Salma Abdel Magid, Yulun Zhang, Donglai Wei, Won-Dong Jang, Zudi Lin, Yun Fu, and Hanspeter Pfister. 2021. Dynamic high-pass filtering and multi-spectral attention for image super-resolution. In Int. Conf. Comput. Vis. 4288--4297.Google ScholarGoogle ScholarCross RefCross Ref
  26. Rafa? Mantiuk, Kil Joong Kim, Allan G Rempel, and Wolfgang Heidrich. 2011. HDR-VDP-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions. ACM Trans. Graph. 30, 4 (2011), 1--14.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee. 2017. Deep multi-scale convolutional neural network for dynamic scene deblurring. In IEEE Conf. Comput. Vis. Pattern Recog. 3883--3891.Google ScholarGoogle ScholarCross RefCross Ref
  28. Zequn Qin, Pengyi Zhang, Fei Wu, and Xi Li. 2021. Fcanet: Frequency channel attention networks. In Int. Conf. Comput. Vis. 783--792.Google ScholarGoogle ScholarCross RefCross Ref
  29. Daniele Ravì, Miroslaw Bober, Giovanni Maria Farinella, Mirko Guarnera, and Sebastiano Battiato. 2016. Semantic segmentation of images exploiting DCT based features and random forest. Pattern Recogn. 52 (2016), 260--273.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Xing Shen, Jirui Yang, Chunbo Wei, Bing Deng, Jianqiang Huang, Xian-Sheng Hua, Xiaoliang Cheng, and Kewei Liang. 2021. Dct-mask: Discrete cosine transform mask representation for instance segmentation. In IEEE Conf. Comput. Vis. Pattern Recog. 8720--8729.Google ScholarGoogle ScholarCross RefCross Ref
  31. Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and ZehanWang. 2016. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In IEEE Conf. Comput. Vis. Pattern Recog. 1874--1883.Google ScholarGoogle ScholarCross RefCross Ref
  32. Hang Su, Varun Jampani, Deqing Sun, Orazio Gallo, Erik Learned-Miller, and Jan Kautz. 2019. Pixel-adaptive convolutional neural networks. In IEEE Conf. Comput. Vis. Pattern Recog. 11166--11175.Google ScholarGoogle ScholarCross RefCross Ref
  33. Jian Sun, Wenfei Cao, Zongben Xu, and Jean Ponce. 2015. Learning a convolutional neural network for non-uniform motion blur removal. In IEEE Conf. Comput. Vis. Pattern Recog. 769--777.Google ScholarGoogle ScholarCross RefCross Ref
  34. IT Union. 2015. Recommendation ITU-R BT. 709--6. Electronic Publication (2015).Google ScholarGoogle Scholar
  35. IT Union. 2016. Recommendation ITU-R BT. 2100--2. Electronic Publication (2016).Google ScholarGoogle Scholar
  36. IT Union. 2019. Recommendation ITU-R BT. 2124-0. Electronic Publication (2019).Google ScholarGoogle Scholar
  37. Yunhe Wang, Chang Xu, Shan You, Dacheng Tao, and Chao Xu. 2016. Cnnpack: Packing convolutional neural networks in the frequency domain. Adv. Neural Inform. Process. Syst. 29 (2016).Google ScholarGoogle Scholar
  38. Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 4 (2004), 600--612.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Jie Wei. 2002. Image segmentation based on situational DCT descriptors. Pattern Recogn. 23, 1--3 (2002), 295--302.Google ScholarGoogle Scholar
  40. Wenbin Xie, Dehua Song, Chang Xu, Chunjing Xu, Hui Zhang, and Yunhe Wang. 2021. Learning Frequency-aware Dynamic Network for Efficient Super- Resolution. In Int. Conf. Comput. Vis. 4308--4317.Google ScholarGoogle Scholar
  41. Hui Zeng, Jianrui Cai, Lida Li, Zisheng Cao, and Lei Zhang. 2020. Learning image-adaptive 3d lookup tables for high performance photo enhancement in real-time. IEEE Trans. Pattern Anal. Mach. Intell. (2020).Google ScholarGoogle ScholarCross RefCross Ref
  42. Kai Zhang, Yawei Li,Wangmeng Zuo, Lei Zhang, Luc Van Gool, and Radu Timofte. 2021. Plug-and-play image restoration with deep denoiser prior. IEEE Trans. Pattern Anal. Mach. Intell. (2021).Google ScholarGoogle Scholar
  43. Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. 2017. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Trans. Image Process. 26, 7 (2017), 3142--3155.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Kai Zhang, Wangmeng Zuo, Shuhang Gu, and Lei Zhang. 2017. Learning deep CNN denoiser prior for image restoration. In IEEE Conf. Comput. Vis. Pattern Recog. 3929--3938.Google ScholarGoogle ScholarCross RefCross Ref
  45. Lin Zhang and Hongyu Li. 2012. SR-SIM: A fast and high performance IQA index based on spectral residual. In IEEE Int. Conf. Image Process. IEEE, 1473--1476.Google ScholarGoogle ScholarCross RefCross Ref
  46. Yulun Zhang, Kunpeng Li, Kai Li, LichenWang, Bineng Zhong, and Yun Fu. 2018. Image super-resolution using very deep residual channel attention networks. In Eur. Conf. Comput. Vis. 286--301.Google ScholarGoogle ScholarCross RefCross Ref
  47. Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Int. Conf. Comput. Vis. 2223--2232.Google ScholarGoogle ScholarCross RefCross Ref
  48. Xueyan Zou, Fanyi Xiao, Zhiding Yu, and Yong Jae Lee. 2020. Delving Deeper into Anti-aliasing in ConvNets. In Brit. Mach. Vis. Conf.Google ScholarGoogle Scholar

Index Terms

  1. FMNet: Frequency-Aware Modulation Network for SDR-to-HDR Translation

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        MM '22: Proceedings of the 30th ACM International Conference on Multimedia
        October 2022
        7537 pages
        ISBN:9781450392037
        DOI:10.1145/3503161

        Copyright © 2022 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 10 October 2022

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate995of4,171submissions,24%

        Upcoming Conference

        MM '24
        MM '24: The 32nd ACM International Conference on Multimedia
        October 28 - November 1, 2024
        Melbourne , VIC , Australia

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader