Aprenda a mejorar flujos de espigas dispersas

High-speed vision tasks have long been a challenge in computer vision. Recently, the spike camera has shown great potential in these tasks due to its high temporal resolution. Unlike traditional cameras, it emits asynchronous spike signals to capture visual information. However, under low-light conditions, spike signals becomehighly sparse, and the sparse spike streamseverely hinders theeffectiveness of existing spike-based methods in high-speed scenarios. To address this challenge,we introduce SS2DS, the first deep learning framework that enhances sparse spike streams into dense spike streams. SS2DS first estimates the spike firing frequency within sparse streams. Subsequently, the spike firing frequency is enhanced by a neural network. Finally, SS2DS decodes the enhanced spike stream from the enhanced spike firing frequency sequence. SS2DS can adjust the temporal distribution of sparse spike streams and improve the performance degradation of existing methods in low-light and high-speed scenarios. In order to evaluate sparse spikestream enhancement,we construct both synthetic and real sparse spike stream datasets. The real dataset iscollected in dynamic scenarios using the third-generation spike camera.By comparing the reconstruction results, enhanced spike streams achieve an average improvement of +0.78 MA, -18.42 BRISQUE, and -1.42 NIQE over sparse spike streams. Moreover, the enhanced spike streams also benefit other spike-based vision tasks, such as 3D reconstruction (+1.325 dB PSNR, +0.005 SSIM, and -0.01 LPIPS) and super-resolution (+0.63 MA, -13.67 BRISQUE, and -1.28 NIQE). Code and datasets will be released after publication.