Learning Differential Pyramid Representation for Tone Mapping

• URL: http://arxiv.org/abs/2412.01463v1
• Date: Mon, 02 Dec 2024 12:59:46 GMT
• Title: Learning Differential Pyramid Representation for Tone Mapping
• Authors: Qirui Yang, Yinbo Li, Peng-Tao Jiang, Qihua Cheng, Biting Yu, Yihao Liu, Huanjing Yue, Jingyu Yang,
• Abstract summary: We introduce a learnable Differential Pyramid Representation Network (DPRNet)<n>DPRNet can capture detailed textures and structures, which is crucial for high-quality tone mapping recovery.<n>In addition, to achieve global consistency and local contrast, we design a global tone perception module and a local tone tuning module.
• Score: 17.030166961019166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Previous tone mapping methods mainly focus on how to enhance tones in low-resolution images and recover details using the high-frequent components extracted from the input image. These methods typically rely on traditional feature pyramids to artificially extract high-frequency components, such as Laplacian and Gaussian pyramids with handcrafted kernels. However, traditional handcrafted features struggle to effectively capture the high-frequency components in HDR images, resulting in excessive smoothing and loss of detail in the output image. To mitigate the above issue, we introduce a learnable Differential Pyramid Representation Network (DPRNet). Based on the learnable differential pyramid, our DPRNet can capture detailed textures and structures, which is crucial for high-quality tone mapping recovery. In addition, to achieve global consistency and local contrast harmonization, we design a global tone perception module and a local tone tuning module that ensure the consistency of global tuning and the accuracy of local tuning, respectively. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art methods, improving PSNR by 2.58 dB in the HDR+ dataset and 3.31 dB in the HDRI Haven dataset respectively compared with the second-best method. Notably, our method exhibits the best generalization ability in the non-homologous image and video tone mapping operation. We provide an anonymous online demo at https://xxxxxx2024.github.io/DPRNet/.

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