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Robust Single-Image Super-Resolution Based on Adaptive Edge-Preserving Smoothing Regularization.

Shuying Huang, Jun Sun, Yong Yang

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    This study introduces a robust edge-preserving smoothing super-resolution (REPS-SR) method. It effectively reduces noise and preserves edges in low-resolution images for better super-resolution reconstruction.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Signal Processing

    Background:

    • Single-image super-resolution (SR) is crucial for enhancing image detail.
    • Image degradation, such as noise and blur, significantly impacts SR performance.
    • Sparse representation is a popular framework for SR reconstruction.

    Purpose of the Study:

    • To develop a novel robust edge-preserving smoothing super-resolution (REPS-SR) method.
    • To improve SR performance on noisy low-resolution (LR) images.
    • To create a method that is robust to varying noise levels without manual tuning.

    Main Methods:

    • Proposed a REPS-SR method within the sparse representation framework.
    • Introduced an edge-preserving smoothing (EPS) regularization term using gradient-domain-guided filtering.
    • Developed an adaptive smoothing-aware factor based on estimated noise levels for optimal regularization balance.
    • Employed an iterative shrinkage algorithm for reconstructing SR images.

    Main Results:

    • The REPS-SR method effectively preserves image edges.
    • The method successfully reduces noise in the reconstructed images.
    • Experimental results demonstrate superior performance on noisy images compared to state-of-the-art methods.
    • The adaptive smoothing-aware factor ensures robustness across different noise levels.

    Conclusions:

    • The proposed REPS-SR method offers a robust solution for super-resolution on noisy images.
    • Edge preservation and noise reduction are significantly improved.
    • The adaptive approach enhances the method's applicability and performance in real-world scenarios.