目的 针对现有图像压缩感知重建方法存在稀疏与收缩机制相互独立、稀疏度与收缩阈值固定、率失真优化缺失、低采样率适配性差,以及深层网络易梯度消失、训练不稳定、抗干扰能力弱、实际场景泛化性不足等问题,提出一种稀疏约束与收缩协同优化的图像压缩感知重建模型S&SCCA。方法 构建模型在处理机制和建模方式2个方面有所突破,将稀疏约束与收缩机制统一在同一损失函数,突破两者独立的惯例。引入的自适应稀疏系数,实现了稀疏强度随图像特征复杂度动态调整,定义的可学习收缩阈值,确保了收缩阈值能够随采样率端到端学习。提出的多目标联合损失控制策略,将重构、稀疏、收缩、码率、正则化协同优化纳入统一规划。结果 相关实验表明,协同优化机制、自适应策略、残差收缩结构与率失真优化可逐级提升图像重构精度、维持模型训练稳定、满足低采样高压缩比需求。在0.1低采样率下,检测的PSNR为39.05 dB,SSIM值为0.84,训练与验证损失差为0.06,VIF值为0.73,EPI值为0.82,收敛速度提升近40%,压缩效率和推理速度表现良好。模型在实际工程应用场景下,总体性能表现良好、稳定。结论 S&SCCA模型通过稀疏与收缩协同、自适应动态调整、残差轻量化结构与率失真联合优化,有效解决图像压缩编码和解码重构中存在的关键问题,在复杂环境与噪声干扰下,具备一定的泛化能力和工程应用价值,可为图像压缩感知、轻量化重建与抗干扰传输提供了有效解决方案。
Abstract
To tackle the limitations of existing image compressive sensing reconstruction methods, such as the separation of sparsity constraints and shrinkage mechanisms, fixed sparsity and shrinkage threshold values, absence of rate-distortion optimization, and poor adaptability at low sampling ratios, along with common deep network defects including vanishing gradients, unstable training, weak anti-noise performance and limited generalization ability for practical scenarios, the work aims to propose the S&SCCA, an image compressive sensing reconstruction model based on joint sparsity and shrinkage collaborative optimization. The constructed model achieved breakthroughs in both processing mechanism and modeling paradigm. It unified sparsity constraints and shrinkage mechanisms within a single loss function, breaking the conventional practice where the two modules operated independently. The introduced adaptive sparse coefficients enabled dynamic adjustment of sparsity intensity according to the complexity of image features, while the defined learnable shrinkage thresholds allowed end-to-end optimization of threshold values varying with sampling ratios. Moreover, a multi-objective joint loss control strategy was proposed to integrate the collaborative optimization of reconstruction, sparsity, shrinkage, bit rate and regularization into an integrated optimization framework. Relevant experiments demonstrated that the collaborative optimization mechanism, adaptive strategy, residual shrinkage structure and rate-distortion optimization could progressively boost image reconstruction accuracy, stabilize model training, and satisfy the requirements of high compression ratios under low sampling rates. At the low sampling ratio of 0.1, the model achieved a PSNR of 39.05 dB, an SSIM of 0.84, a training-validation loss gap of 0.06, a VIF of 0.73 and an EPI of 0.82, with nearly 40% faster convergence. It delivered favorable compression efficiency and inference speed, and exhibited outstanding and stable overall performance in practical engineering scenarios. The S&SCCA model effectively addresses core challenges in image compression coding and decoding reconstruction via collaborative sparsity-shrinkage optimization, adaptive dynamic adjustment, lightweight residual architecture, and joint rate-distortion optimization. It possesses favorable generalization capability under complex environments and noise interference, and holds practical engineering application value. This method can serve as an effective solution for image compressive sensing, lightweight reconstruction, and anti-interference transmission.
关键词
图像压缩感知 /
稀疏与收缩协同优化 /
可学习阈值 /
残差收缩 /
率失真优化 /
鲁棒重构
Key words
image compressive sensing /
joint sparsity and shrinkage collaborative optimization /
learnable threshold /
residual shrinkage /
rate-distortion optimization /
robust reconstruction
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