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Related Experiment Video

Updated: Aug 29, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
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Toward Pixel-Level Precision for Binary Super-Resolution With Mixed Binary Representation.

Xinrui Jiang, Nannan Wang, Jingwei Xin

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    |September 7, 2022
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    Summary
    This summary is machine-generated.

    Binary neural networks (BNNs) for super-resolution (SR) are improved by a new precision-driven binary convolution (PDBC) module. This approach reduces information loss and enhances image quality, narrowing the performance gap with full-precision models.

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    Area of Science:

    • Computer Vision
    • Deep Learning
    • Image Processing

    Background:

    • Binary neural networks (BNNs) offer computational and memory efficiency for super-resolution (SR).
    • A performance gap persists between BNNs and full-precision networks in SR tasks.
    • Quantization in BNNs reduces feature information density, impacting SR performance.

    Purpose of the Study:

    • To enhance the precision of quantization features in BNNs for improved SR.
    • To compensate for quantization precision loss using a novel mixed binary representation.
    • To introduce a precision-driven binary convolution (PDBC) module to preserve image details.

    Main Methods:

    • Approximating multibit values using multiple 1-bit values.
    • Developing a mixed binary representation set to approximate multibit activations.
    • Implementing a precision-driven binary convolution (PDBC) module to increase convolution precision.

    Main Results:

    • The proposed methods significantly reduce information loss from binarization.
    • The PDBC module enhances convolution precision without additional computational cost.
    • Experimental results demonstrate superior performance over baseline and state-of-the-art SR methods.

    Conclusions:

    • The mixed binary representation effectively compensates for quantization precision loss in SR.
    • The precision-driven binary convolution (PDBC) module improves SR performance and visual quality.
    • This work advances BNNs for SR by bridging the performance gap with full-precision models.