The existing techniques for simultaneous encryption and compression of images refer lossy compression. Their reconstruction performances did not meet the accuracy of medical images because most of them have not been applicable to three-dimensional (3D) medical image volumes intrinsically represented by tensors. We propose a tensor-based algorithm using tensor compressive sensing (TCS) to address these issues. Alternating least squares is further used to optimize the TCS with measurement matrices encrypted by discrete 3D Lorenz. The proposed method preserves the intrinsic structure of tensor-based 3D images and achieves a better balance of compression ratio, decryption accuracy, and security. Furthermore, the characteristic of the tensor product can be used as additional keys to make unauthorized decryption harder. Numerical simulation results verify the validity and the reliability of this scheme.

Detecting text within medical images presents a formidable challenge in the domain of computer vision due to the intricate nature of textual backgrounds, the dense text concentration, and the possible existence of extreme aspect ratios. This paper introduces an effective and precise text detection system tailored to address these challenges. The system incorporates an optimized segmentation module, a trainable post-processing method, and leverages a vision-language pre-training model (oCLIP). Specifically, our segmentation head integrates three essential components: the Feature Pyramid Network (FPN) module, which combines a residual structure and channel attention mechanism; the Efficient Feature Enhancement Module (EFEM); and the Multi-Scale Feature Fusion with RSEConv (MSFM-RSE), designed specifically for multi-scale feature fusion based on RSEConv. By introducing a residual structure and channel attention mechanism into the FPN module, the convolutional layers are replaced with RSEConv layers that employ a channel attention mechanism, further augmenting the representational capacity of the feature maps. The EFEM, designed as a cascaded U-shaped module, incorporates a spatial attention mechanism to introduce multi-level information, thereby enhancing segmentation performance. Subsequently, the MSFM-RSE adeptly amalgamates features from various depths and scales of the EFEM to generate comprehensive final features tailored for segmentation purposes. Additionally, a post-processing module employs a differentiable binarization strategy, allowing the segmentation network to dynamically determine the binarization threshold. Building on the system’s improvement, we introduce a vision-language pre-training model that undergoes extensive training on various visual language understanding tasks. This pre-trained model acquires detailed visual and semantic representations, further reinforcing both the accuracy and robustness in text detection when integrated with the segmentation module. The performance of our proposed model was evaluated through experiments on medical text image datasets, demonstrating excellent results. Multiple benchmark experiments validate its superior performance in comparison to existing methods. Code is available at: https://github.com/csworkcode/VLDBNet.