Real-time high-quality single-lens computational imaging via enhancing lens modulation transfer function consistency.

The demand for high-quality, lightweight infrared imaging systems is rapidly increasing. Single-lens computational imaging, combining single-lens with post-processing algorithms, offers a promising solution to miniaturize imaging systems while maintaining performance. However, these post-processing algorithms are typically highly complex, posing significant challenges for real-time reconstruction on a neural network processing unit (NPU) chip. This study investigates the relationship between the complexity of post-processing algorithms and lens modulation transfer function (MTF), demonstrating that a single-lens system with highly consistent MTF can significantly reduce the complexity of post-processing algorithms. Building on this insight, we proposed an enhancing lens MTF consistency-based single-lens design method and developed a single-lens computational infrared imaging system featuring a small Res-Unet (S-Res-Unet) neural network. Compared to the traditional method using the large Res-Unet (L-Res-Unet) to achieve comparable reconstruction performance, the proposed system achieved a 16-fold reduction in computational demands. As a result, it performed real-time reconstructions at the rate of 25 frames per second (fps) with a resolution of 640×480 on the RK3588 NPU chip, while maintaining system MTF exceeding 0.42 at Nyquist frequency of 42 lp/mm. This research paves the way for the practical application of computational infrared imaging systems.