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Receptive Field Size vs. Model Depth for Single Image Super-resolution.

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    Single image super-resolution (SISR) performance hinges more on receptive field size than model depth. Optimal SISR requires aligning receptive field size with model depth for improved effectiveness and efficiency.

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    Area of Science:

    • Computer Vision
    • Deep Learning
    • Image Processing

    Background:

    • Deep learning models have significantly advanced single image super-resolution (SISR).
    • Key architectural claims include large receptive fields and strong nonlinearity.
    • The relative importance of receptive field size versus model depth for SISR remains unclear.

    Purpose of the Study:

    • To investigate the impact of receptive field size and model depth on SISR performance.
    • To determine which factor, receptive field size or model depth, is more critical for SISR.
    • To guide the design of more efficient and effective SISR architectures.

    Main Methods:

    • Utilized dilated convolutions to systematically vary receptive field sizes.
    • Conducted exhaustive investigations to analyze the effects of these variations on SISR performance.
    • Compared the performance of models with different depths and receptive field sizes.

    Main Results:

    • Single image super-resolution (SISR) performance is more sensitive to changes in receptive field size than to model depth variations.
    • Model depth must be congruent with receptive field size to achieve improved SISR performance.
    • A shallower architecture can achieve comparable effectiveness to deeper ones when designed appropriately.

    Conclusions:

    • Receptive field size is a more critical factor than model depth for single image super-resolution (SISR).
    • Optimizing SISR requires a balance between receptive field size and model depth.
    • The findings enable the development of shallower, computationally efficient SISR models without sacrificing performance.