Single-pixel camera is developed to mitigate the constraints faced by the conventional cameras especially in invisible wavelengths and low light conditions. Nyquist–Shannon theorem requires as many measurements as the image pixels to reconstruct images flawlessly. In practice, obtaining more measurements increases the cost and acquisition time, which are the major drawbacks of single-pixel imaging (SPI). Therefore, compressive sensing was proposed to enable image reconstruction with fewer measurements. We present a design of sensing patterns to obtain image information by utilizing spatially variant resolution (SVR) technique in SPI. The proposed method reduces the measurements by prioritizing the resolution in the region of interest (ROI). It successfully achieves the programmable imaging concept where multiresolution adaptively optimizes the balance between the image quality and the measurements number. Results show that SVR images can be reconstructed from significantly fewer measurements yet able to achieve better image quality than uniform resolution images. In addition, the SVR images can be further enhanced by integrating the dynamic supersampling technique. Consequently, the concerns of image quality, long acquisition, and processing time can be addressed. The proposed method potentially benefits imaging applications where the target ROI is prioritized over the background and most importantly it requires fewer measurements.

中文翻译：

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基于压缩感测的可编程空间变异单像素成像

开发单像素相机以减轻常规相机面临的限制，尤其是在不可见波长和弱光条件下。奈奎斯特–香农定理需要与图像像素一样多的测量才能完美地重建图像。实际上，获得更多的测量值会增加成本和获取时间，这是单像素成像（SPI）的主要缺点。因此，提出了压缩感测以使得能够以更少的测量来重建图像。我们提出了一种通过在SPI中利用空间变异分辨率（SVR）技术来获取图像信息的传感模式设计。所提出的方法通过优先考虑感兴趣区域（ROI）的分辨率来减少测量。它成功实现了可编程成像概念，其中多分辨率自适应地优化了图像质量和测量次数之间的平衡。结果表明，与均匀分辨率的图像相比，SVR图像可以通过显着减少的测量来重建，但能够获得更好的图像质量。另外，通过集成动态超采样技术可以进一步增强SVR图像。因此，可以解决图像质量，长时间获取和处理时间的问题。所提出的方法可能有益于成像应用，在这些应用中目标ROI的优先级高于背景，最重要的是它需要较少的测量。结果表明，与均匀分辨率的图像相比，SVR图像可以通过显着减少的测量来重建，但能够获得更好的图像质量。此外，通过集成动态超采样技术可以进一步增强SVR图像。因此，可以解决图像质量，长时间获取和处理时间的问题。所提出的方法可能有益于成像应用，在这些应用中目标ROI的优先级高于背景，最重要的是它需要较少的测量。结果表明，与均匀分辨率图像相比，SVR图像可以通过显着减少的测量来重建，但仍可以获得更好的图像质量。此外，通过集成动态超采样技术可以进一步增强SVR图像。因此，可以解决图像质量，长时间获取和处理时间的问题。所提出的方法可能有益于成像应用，在这些应用中目标ROI的优先级高于背景，最重要的是它需要较少的测量。