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Rotation Equivariant Arbitrary-Scale Image Super-Resolution.

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    This study introduces a rotation equivariant method for arbitrary-scale image super-resolution (ASISR) to fix geometric distortions. The new approach enhances image recovery by maintaining structural integrity and original orientations.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Deep Learning

    Background:

    • Arbitrary-scale image super-resolution (ASISR) aims to reconstruct high-resolution images from low-resolution inputs.
    • Current ASISR methods struggle with geometric distortions in low-resolution images, causing artifacts in reconstructions.
    • Rotation equivariance is crucial for preserving the orientation and structure of geometric patterns.

    Purpose of the Study:

    • To develop a novel rotation equivariant method for arbitrary-scale image super-resolution.
    • To address the challenge of geometric pattern warping and deformation in low-resolution images.
    • To achieve end-to-end rotational equivariance in ASISR networks.

    Main Methods:

    • Redesigned encoder and implicit neural representation (INR) modules to incorporate intrinsic rotation equivariance.
    • Developed a method for end-to-end rotational equivariance from input to output.
    • Provided theoretical analysis to evaluate intrinsic equivariance error.

    Main Results:

    • The proposed method successfully maintains the original orientations and structural integrity of geometric patterns.
    • Experiments on simulated and real datasets demonstrate the superiority of the rotation equivariant ASISR approach.
    • The framework can be integrated into existing ASISR methods as a plug-and-play module.

    Conclusions:

    • The developed rotation equivariant ASISR method effectively mitigates artifacts caused by geometric distortions.
    • This work establishes end-to-end rotational equivariance in ASISR for the first time.
    • The proposed approach offers a significant advancement in reconstructing high-fidelity images with preserved structural details.