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In the realm of super-resolution neural networks, the pursuit of enhanced image restoration quality alongside the efficient operation of models has been a paramount focus of research. Nonetheless, numerous existing network architectures confront substantial computational resource consumption and increased deployment intricacies due to their immense parameter counts. Attempts to curtail these parameters for practical applications frequently result in a marked decline in performance, thereby posing a pivotal challenge in the optimization of super-resolution networks. Consequently, devising efficacious optimization strategies that can diminish parameters while preserving or closely approximating original performance levels is of utmost importance. Against this backdrop, this study introduces an innovative optimization framework, wherein the Binary Gannet Optimization Algorithm (BGOA)—a bio-inspired heuristic algorithm mimicking gannet foraging behaviors augmented with binary encoding—serves as the cornerstone search methodology, enabling in-depth refinement of Residual Dense Networks (RDNs). The BGOA adeptly navigates complex search spaces to identify superior network architecture configurations. Comprehensive experimentation across multiple public datasets corroborates the efficacy of this approach in substantially reducing RDN model parameters without compromising on high-performance metrics, thereby highlighting its potential and practical utility in the realm of super-resolution network optimization. This research breakthrough furnishes a fresh avenue for realizing super-resolution image reconstruction techniques that are concurrently efficient and precise.

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Efficient Architecture Search for Super-Resolution Residual Dense Networks via Heuristic Optimization

  • 会议论文

  • 发表日期：

    2026年

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  • Shu-Chuan Chu,
  • Ruhai You,
  • Jeng-Shyang Pan,
  • Pei Hu

摘要

In the realm of super-resolution neural networks, the pursuit of enhanced image restoration quality alongside the efficient operation of models has been a paramount focus of research. Nonetheless, numerous existing network architectures confront substantial computational resource consumption and increased deployment intricacies due to their immense parameter counts. Attempts to curtail these parameters for practical applications frequently result in a marked decline in performance, thereby posing a pivotal challenge in the optimization of super-resolution networks. Consequently, devising efficacious optimization strategies that can diminish parameters while preserving or closely approximating original performance levels is of utmost importance. Against this backdrop, this study introduces an innovative optimization framework, wherein the Binary Gannet Optimization Algorithm (BGOA)—a bio-inspired heuristic algorithm mimicking gannet foraging behaviors augmented with binary encoding—serves as the cornerstone search methodology, enabling in-depth refinement of Residual Dense Networks (RDNs). The BGOA adeptly navigates complex search spaces to identify superior network architecture configurations. Comprehensive experimentation across multiple public datasets corroborates the efficacy of this approach in substantially reducing RDN model parameters without compromising on high-performance metrics, thereby highlighting its potential and practical utility in the realm of super-resolution network optimization. This research breakthrough furnishes a fresh avenue for realizing super-resolution image reconstruction techniques that are concurrently efficient and precise.