While self-attention mechanisms in transformers exhibit superior performance in image super-resolution tasks, improving efficiency remains a challenge. To enhance the efficiency of self-attention mechanisms for stereo image super-resolution, we propose an efficient transformer with compressed attention for stereo image super-resolution (ETCASSR). Specifically, we propose a simple yet effective compressed-attention mechanism that organizes channels from partial to full for attention operations. Using this mechanism, we develop a compressed window-based self-attention block and a compressed transposed self-attention block, enabling efficient intra-view feature extraction. To further enrich feature representation, we introduce a spatial local feature branch and a channel global feature branch to complement these two blocks. Furthermore, a compressed cross-attention block for cross-view feature extraction is designed by extending the compressed-attention mechanism. Combining these blocks, ETCASSR achieves state-of-the-art performance on stereo image super-resolution while maintaining low computational complexity and fast running speed. Additionally, we introduce ETCASR for single-image super-resolution by omitting the cross-view components from ETCASSR, also achieving superior performance with high efficiency. The proposed transformers offer significant potential applications in other vision tasks. Source code will be released at https://github.com/jianwensong/ETCASSR .

Although vision transformers have shown remarkable performance in image super-resolution tasks, the key component, i.e., the self-attention mechanism, suffers from insufficient high-frequency information extraction capability and high computational costs, hindering further advancement. To address these limitations, we propose an efficient frequency feature aggregation transformer for single image super-resolution (EFATSR). Specifically, a frequency self-attention aggregation block is proposed to enhance the extraction of high-frequency information. This block incorporates a frequency spatial feature aggregation branch to supplement high-frequency feature extraction for a self-attention branch, enabling the model to capture high-frequency information more effectively. Additionally, a frequency channel-spatial aggregation block is proposed to extract channel and spatial features in the frequency domain, enhancing the efficiency of deep feature extraction. Extensive experiments on single image super-resolution demonstrate that EFATSR achieves state-of-the-art performance while maintaining low computational complexity. Furthermore, we extend EFATSR for stereo image super-resolution by incorporating a multi-head parallax-attention block, forming EFATSSR, which also shows remarkable performance and high efficiency. Source code is avaliable at https://github.com/jianwensong/EFATSR .

Current stereo image super-resolution methods do not fully exploit cross-view and intra-view information, resulting in limited performance. While vision transformers have shown great potential in super-resolution, their application in stereo image super-resolution is hindered by high computational demands and insufficient channel interaction. This paper introduces an efficient masked feature and group attention network for stereo image super-resolution (EMGSSR) designed to integrate the strengths of transformers into stereo super-resolution while addressing their inherent limitations. Specifically, an efficient masked feature block is proposed to extract local features from critical areas within images, guided by sparse masks. A group-weighted cross-attention module consisting of group-weighted cross-view feature interactions along epipolar lines is proposed to fully extract cross-view information from stereo images. Additionally, a group-weighted self-attention module consisting of group-weighted self-attention feature extractions with different local windows is proposed to effectively extract intra-view information from stereo images. Experimental results demonstrate that the proposed EMGSSR outperforms state-of-the-art methods at relatively low computational costs. The proposed EMGSSR offers a robust solution that effectively extracts cross-view and intra-view information for stereo image super-resolution, bringing a promising direction for future research in high-fidelity stereo image super-resolution. Source codes will be released at https://github.com/jianwensong/EMGSSR .

It is very challenging to fully use cross-view information for stereo image super-resolution. Previous methods using pixel-based parallax-attention mechanisms do not consider neighborhood pixels. Also, they typically use convolutions for basic feature extraction, which may not be as effective as modern self-attention mechanisms in transformers. To address these limitations, we propose an efficient hybrid feature interaction network for stereo image super-resolution. Specifically, we propose a shifted cross-view interaction block that integrates neighborhood pixels and imposes constraints on the disparity range during cross-view interactions. In addition, we propose a hybrid feature interaction block consisting of local and global interaction branches for extracting intra-view features efficiently. In this block, we propose a design that incorporates lightweight attention connections and a partial downsampling operation to enhance spatial and channel feature interaction with high efficiency. Additionally, a dilated efficient channel attention mechanism is proposed to obtain cross-channel interactions within features. Experimental results evaluated on various metrics (PSNR, SSIM, and LPIPS) demonstrate that the proposed method achieves state-of-the-art stereo image super-resolution performance at relatively low computational cost. Moreover, the super-resolution images obtained by the proposed method achieve the smallest stereo matching errors compared to other methods. Source code will be publicly available athttps://github.com/jianwensong/EHFSSR.

Structured light 3D imaging is often used for obtaining accurate 3D information via phase retrieval. Single-pattern structured light 3D imaging is much faster than multi-pattern versions. Current phase retrieval methods for single-pattern structured light 3D imaging are however not accurate enough. Besides, the projector resolution in a structured light 3D imaging system is expensive to improve due to hardware costs. To address the issues of low accuracy and low resolution of single-pattern structured light 3D imaging, this work proposes a super-resolution phase retrieval network (SRPRNet). Specifically, a phase-shifting module is proposed to extract multi-scale features with different phase shifts, and a refinement and super-resolution module is proposed to obtain refined and super-resolution phase components. After phase demodulation and unwrapping, high-resolution absolute phase is obtained. A sine shifting loss and a cosine shifting loss are also introduced to form the regularization term of the loss function. As far as can be ascertained, the proposed SRPRNet is the first network for super-resolution phase retrieval by using a single pattern, and it can also be used for standard-resolution phase retrieval. Experimental results on three datasets show that SRPRNet achieves state-of-the-art performance on $1\times $ , $2\times $ , and $4\times $ super-resolution phase retrieval tasks.

With the process of China's entering a serious aging society, it generally becomes one of the problems for people caring as elderly falls frequently. Thus, it is necessary for us find a way that the old people get help timely when it happened. On studies related to falls in outdoors currently, popular methods are on Computer Vision and Inertial Sensor. The problems of these researches are focus on the fall phenomenon of elderly mostly, lacking of deep excavation and analysis of experimental data. This paper is bringing forward Ë(() Set theory that means `Changing Set', which forms a dynamic set of the interrelated elements with FR (Frame of reference), D(t) (Experimental numerical set), T (Computing method set) and P (Results reasoning analysis set). We study the reasoning algorithmic using Ë(t) Set with normalized component data (both the angular velocity and acceleration from Inertial Sensor), to define feature points of the Ë(t) Set and determine the automatic alarm status of the fall time of the elderly.