Spatial mapping of thermal properties is critical for unveiling the structure-property relation of materials, heterogeneous interfaces, and devices. These property images can also serve as datasets for training artificial intelligence models for material discoveries and optimization. Here, we introduce a high-throughput thermal-property imaging method called compressive-sensing frequency-domain thermoreflectance (CS-FDTR), which can robustly profile thermal-property distributions with micrometer resolutions while requiring only a random subset of pixels to be experimentally measured. The high-resolution thermal-property image is reconstructed from the raw down-sampled data through ${\mathcal{L}}_1$-regularized minimization. The high-throughput imaging capability of CS-FDTR is validated using the following cases: (a) the thermal conductance of a patterned heterogeneous interface, (b) the thermal conductivity variations of an annealed pyrolytic graphite sample, and (c) the sharp change in thermal conductivity across a vertical aluminum/graphite interface. With less than half of the pixels being experimentally sampled, the thermal-property images measured using CS-FDTR show good agreement with the ground truth (point-by-point scanning), with a relative deviation below 15%. This work opens the possibility of high-throughput thermal-property imaging without sacrificing the data quality, which is critical for materials discovery and screening.

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• Imaging & optical processing
• Structural properties
• Thermal properties
• Transport phenomena
• Pump-probe spectroscopy
• Thermoreflectance