Frequency Enhancement for Image Demosaicking

• URL: http://arxiv.org/abs/2503.15800v1
• Date: Thu, 20 Mar 2025 02:37:10 GMT
• Title: Frequency Enhancement for Image Demosaicking
• Authors: Jingyun Liu, Daiqin Yang, Zhenzhong Chen,
• Abstract summary: We propose Dual-path Frequency Enhancement Network (DFENet), which reconstructs RGB images in a divide-and-conquer manner.<n>One path focuses on generating missing information through detail refinement in spatial domain, while the other aims at suppressing undesirable frequencies.<n>With these designs, the proposed DFENet outperforms other state-of-the-art algorithms on different datasets.
• Score: 40.76899837631637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recovering high-frequency textures in image demosaicking remains a challenging issue. While existing methods introduced elaborate spatial learning methods, they still exhibit limited performance. To address this issue, a frequency enhancement approach is proposed. Based on the frequency analysis of color filter array (CFA)/demosaicked/ground truth images, we propose Dual-path Frequency Enhancement Network (DFENet), which reconstructs RGB images in a divide-and-conquer manner through fourier-domain frequency selection. In DFENet, two frequency selectors are employed, each selecting a set of frequency components for processing along separate paths. One path focuses on generating missing information through detail refinement in spatial domain, while the other aims at suppressing undesirable frequencies with the guidance of CFA images in frequency domain. Multi-level frequency supervision with a stagewise training strategy is employed to further improve the reconstruction performance. With these designs, the proposed DFENet outperforms other state-of-the-art algorithms on different datasets and demonstrates significant advantages on hard cases. Moreover, to better assess algorithms' ability to reconstruct high-frequency textures, a new dataset, LineSet37, is contributed, which consists of 37 artificially designed and generated images. These images feature complex line patterns and are prone to severe visual artifacts like color moir\'e after demosaicking. Experiments on LineSet37 offer a more targeted evaluation of performance on challenging cases. The code and dataset are available at https://github.com/VelvetReverie/DFENet-demosaicking.

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