mlsanityData to 7 Oct 2026

Multi-Curve Translator for High-Resolution Photorealistic Image Translation

arXiv 2203.07756 ↗First posted 15 Mar 2022cs.CVcited by 3

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Abstract (arXiv)

The dominant image-to-image translation methods are based on fully convolutional networks, which extract and translate an image's features and then reconstruct the image. However, they have unacceptable computational costs when working with high-resolution images. To this end, we present the Multi-Curve Translator (MCT), which not only predicts the translated pixels for the corresponding input pixels but also for their neighboring pixels. And if a high-resolution image is downsampled to its low-resolution version, the lost pixels are the remaining pixels' neighboring pixels. So MCT makes it possible to feed the network only the downsampled image to perform the mapping for the full-resolution image, which can dramatically lower the computational cost. Besides, MCT is a plug-in approach that utilizes existing base models and requires only replacing their output layers. Experiments demonstrate that the MCT variants can process 4K images in real-time and achieve comparable or even better performance than the base models on various photorealistic image-to-image translation tasks.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 49 of its 73 references that are in the map. First posted on arXiv 15 Mar 2022.

Builds on

49 of 73 references.

References · 49

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1.

3 papers.

  1. 2022Vision Transformers for Single Image Dehazingcited by 255
  2. 2024Every Pixel Has its Moments: Ultra-High-Resolution Unpaired Image-to-Image Translation via Dense Normalizationcited by 6
  3. 2025Image Translation with Kernel Prediction Networks for Semantic Segmentationcited by 0