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<p>Real-time denoising of ultrasound images is essential for clinical and healthcare workflows, yet remains constrained by the computational and energy cost and latency of convolution-based filtering on resource- and energy-constrained embedded platforms. This paper presents a unified, automated design space exploration (DSE) framework for hardware- and energy-efficient ultrasound image denoising using approximate Gaussian filtering. The framework integrates approximate computing with multi-objective and many-objective evolutionary optimization to jointly balance image quality, power, area, and critical-path delay. A pipelined two-dimensional Gaussian filter architecture is employed, in which approximate adders and multipliers are jointly selected from the Evolutionary Approximation 8-bit (EvoApprox8b) arithmetic library, enabling circuit-level exploration of accuracy–efficiency trade-offs. Four DSE schemes are evaluated with increasing decision-space complexity and objective dimensionality. Nondominated Sorting Genetic Algorithm II (NSGA-II), Nondominated Sorting Genetic Algorithm III (NSGA-III), and Multi-Objective Particle Swarm Optimization (MOPSO) are systematically compared against random search. A task-driven composite metric, the Quality–Area–Power Product (QUAP), is introduced to support quality-aware ranking of hardware-efficient solutions. Experiments on representative ultrasound images show that NSGA-II achieves the most favorable trade-offs in three-objective optimization, with up to 90.1% reduction in power–area product while maintaining high reconstruction quality (Peak Signal-to-Noise Ratio (PSNR) &gt; 35&#xa0;dB, Structural Similarity Index Measure (SSIM) &gt; 0.93). In many-objective settings with timing constraints, NSGA-III provides superior diversity and robustness. Overall, the proposed framework enables scalable synthesis of approximate Gaussian filtering accelerators for energy-efficient embedded imaging systems, supporting sustainable and accessible healthcare technologies.</p>

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Multiobjective framework for hardware and quality aware approximate Gaussian filtering toward energy efficient ultrasound image denoising

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月1日

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  • Sawaira Sana,
  • Arslan Shaukat,
  • Sajid Gul Khawaja,
  • Ayman Qahmash

摘要

Real-time denoising of ultrasound images is essential for clinical and healthcare workflows, yet remains constrained by the computational and energy cost and latency of convolution-based filtering on resource- and energy-constrained embedded platforms. This paper presents a unified, automated design space exploration (DSE) framework for hardware- and energy-efficient ultrasound image denoising using approximate Gaussian filtering. The framework integrates approximate computing with multi-objective and many-objective evolutionary optimization to jointly balance image quality, power, area, and critical-path delay. A pipelined two-dimensional Gaussian filter architecture is employed, in which approximate adders and multipliers are jointly selected from the Evolutionary Approximation 8-bit (EvoApprox8b) arithmetic library, enabling circuit-level exploration of accuracy–efficiency trade-offs. Four DSE schemes are evaluated with increasing decision-space complexity and objective dimensionality. Nondominated Sorting Genetic Algorithm II (NSGA-II), Nondominated Sorting Genetic Algorithm III (NSGA-III), and Multi-Objective Particle Swarm Optimization (MOPSO) are systematically compared against random search. A task-driven composite metric, the Quality–Area–Power Product (QUAP), is introduced to support quality-aware ranking of hardware-efficient solutions. Experiments on representative ultrasound images show that NSGA-II achieves the most favorable trade-offs in three-objective optimization, with up to 90.1% reduction in power–area product while maintaining high reconstruction quality (Peak Signal-to-Noise Ratio (PSNR) > 35 dB, Structural Similarity Index Measure (SSIM) > 0.93). In many-objective settings with timing constraints, NSGA-III provides superior diversity and robustness. Overall, the proposed framework enables scalable synthesis of approximate Gaussian filtering accelerators for energy-efficient embedded imaging systems, supporting sustainable and accessible healthcare technologies.