A Capacitive Screen Super-Resolution Imaging Method Based on Multi-Frame Aggregation
With the widespread adoption of smart devices,capacitive touchscreens("capacitive screens")have become increasingly popular.However,further development is constrained by its relatively low imaging resolution due to the hardware design and imaging principles.To overcome this limitation,we propose a super-resolution imaging method that reconstructs high-resolution capacitive images by aggregating multiple frames captured during the movement of conductive objects.The proposed super-resolution algorithm comprises three essentials procedures:preprocessing,image reconstruction,and visual optimization.In the preprocessing stage,YOLOv8n and Nano tracker are employed respectively for object detection and object tracking,enabling background noise removal,thus resulting in clean low-resolution capacitive image sequences.During the image reconstruction phase,each frame undergoes Lanczos upsampling,alignment,and merging,ensuring the preservation of image detail and fidelity.Finally,the visual optimization stage introduces a capacitive image deblurring algorithm,which,as validated by experiments,exhibits the best deblurring effect,effectively enhancing image clarity and edge details.Extensive experiments are conducted on a large dataset to evaluate the effectiveness and necessity of the super-resolution algorithm proposed in this paper.The experimental results demonstrate significant advantages of the proposed algorithm in terms of imaging performance,and effectively improve the image resolution and visual quality of capacitive images.
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