Abstract:

Compressive sensing (CS) has been successfully combined with deep-learning neural networks to generate models for image-depth CS. Many existing models face significant limitations, such as their inability to handle tasks with multiple CS ratios and suboptimal performance in image reconstruction. To overcome these challenges, an optimized deep network model, named global multi-attention condition module (GMCM) iterative shrinkage-thresholding algorithm (ISTA)-Net⁺⁺ is proposed. In this model, the CS ratio of an image is incorporated as a network parameter, enabling the network to effectively handle multiple CS ratio tasks. Additionally, a hybrid attention mechanism is employed to preprocess the reconstructed image for feature extraction and to calculate the image complexity score. These elements are then combined with the CS ratio to jointly predict the parameters and iterative reconstruction steps, which are subsequently broadcast to all iterative stages of the model. Extensive experiments on high- and low-resolution image datasets demonstrate that the proposed GMCM ISTA-Net⁺⁺ model achieves a superior performance compared to traditional models. Given its flexibility, enhanced reconstruction quality, and adaptability, this model holds significant potential for deployment in resource-constrained mobile image-processing terminals.

Key words: compressive sensing (CS), hybrid attention mechanism, multiple ratio solver, image feature extraction