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SinColor: Uncertainty-Guided Single-Step Diffusion for Image Colorization.

Yutong Gao, Ying Zhang, Congyan Lang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 7, 2026
    PubMed
    Summary
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    This study introduces a novel single-step diffusion framework for efficient image colorization. The new method significantly reduces computational cost and improves color consistency, achieving state-of-the-art results.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Image colorization aims to add realistic color to grayscale images.
    • Diffusion models have advanced image generation but multi-step processes are inefficient for colorization.
    • Existing methods suffer from color artifacts and high computational costs.

    Purpose of the Study:

    • To develop a novel single-step diffusion framework for efficient and high-quality image colorization.
    • To address limitations of multi-step diffusion models in colorization tasks.
    • To reduce computational cost and improve color consistency.

    Main Methods:

    • Proposed a single-step diffusion framework for image colorization.
    • Introduced a color uncertainty estimation (CUE) module to guide the diffusion process.

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  • Utilized latent encoding and efficient decoding for low-frequency color generation.

    • Main Results:

    • Achieved state-of-the-art performance on ImageNet, COCO-Stuff, and Extended COCO-Stuff datasets.
    • Reduced inference time by 98% and trainable parameters by 97% compared to multi-step methods.
    • Demonstrated high-quality and spatially coherent colorization results.

    Conclusions:

    • The proposed single-step diffusion framework offers a highly efficient and effective solution for image colorization.
    • The uncertainty-aware approach successfully mitigates color artifacts and improves global consistency.
    • This work presents a significant advancement in practical and high-performance image colorization.