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- W4315487231 abstract "This paper proposes a chest X-ray image super-resolution reconstruction method - Deep Contrast Consistent Feature Network, which articulates the contrast consistency as a task to learn spatial contrast enhancement curve followed by a depth contrast network. The proposed method trains a network with visual quality measures through learning non-reference loss - Contrast Consistency Loss, which aims to overcome contrast overstretching and contrastive variation. Specifically, this network does not need to reference images at the time of loss formulation. We evaluated the proposed method for chest X-ray 5K datasets over several benchmarks metrics and models for quantitative and qualitative analysis. The extensive experiments report that the proposed model outperforms other approaches." @default.
- W4315487231 created "2023-01-11" @default.
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- W4315487231 date "2023-01-01" @default.
- W4315487231 modified "2023-09-27" @default.
- W4315487231 title "Chest X-ray Image Super-Resolution via Deep Contrast Consistent Feature Network" @default.
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- W4315487231 doi "https://doi.org/10.1007/978-3-031-23599-3_7" @default.
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