SALR-Former: Learning Ratio- and State-Adaptive Reconstruction for Image Compressive Sensing

Image compressive sensing (ICS) reconstructs images from highly undersampled measurements. Most ICS networks are optimized for fixed sampling ratios, whereas existing arbitrary-ratio methods mainly make a shared sensing or reconstruction pipeline compatible with multiple ratios. Such compatibility does not specify how reconstruction should change during a finite unfolding process: the sampling ratio changes the measurement-constrained image subspace, while the evolving iterate determines the residual errors that remain to be corrected. We propose SALR-Former, a unified unfolding network for arbitrary-ratio ICS. Its Ratio-Stage Controller (RSC) uses the sampling ratio as a global sensing condition and combines it with the stage index, measurement residual, reconstruction feature, and cross-stage memory to control two complementary mechanisms: a bounded physics-update step size and lightweight FiLM-LoRA modulation of the learned prior. The shared prior integrates local, contextual, and frequency-domain representations, while the predicted controls specialize its behavior without ratio-specific backbones or adapter banks. Experiments on five benchmarks demonstrate strong reconstruction quality across trained and unseen continuous sampling ratios and controlled ablations further show that the sampling ratio determines the dominant reconstruction regime, whereas stage and cross-stage history refine the control trajectory.