Salient Object Detection Toward Single-Pixel Imaging

Replacing CCD and CMOS image sensors in conventional cameras with digital micromirror devices (DMD), single-pixel cameras low-costly shot images by capturing compressed measurements and computation. However, the compressed measurements lack explicit spatial information, causing difficulties for high-level tasks such as salient object detection (SOD) that are usually designed to have visual inputs. To address the issue, we propose a single-pixel imaging-based SOD network called SPISODNet that enables predicting saliency maps directly from compressed measurements with high accuracy. Specifically, we first design an underlying feature inversion module (UFIM) to capture the underlying scene information, and then develop a context-aware flow (CAF) consisting of a feature focus module (FFM), three bidirectional attention modules (BAMs), and a spatial information-induced attention module (SIAM) to acquire and polish saliency predictions. Extensive experiments demonstrate that our method achieves superior performance for single-pixel imaging-based SOD.