Fig. 1. Wavefront reconstruction process of compressive wavefront sensor

Fig. 2. Deep neural network structure

Fig. 3. （a）X-direction slopes of the original wavefront；（b）X-direction slopes recovered by GCWS；（c）Error of the x-direction slopes recovered by GCWS；（d）X-direction slopes recovered by DNNCWS；（e）X-direction slopes error recovered by DNNCWS；（f）Y-direction slopes of the original wavefront；（g）Y-direction slopes recovered by GCWS；（h）Error of the y-direction slopes recovered by GCWS；（i）Y-direction slopes recovered by DNNCWS；（j）Y-direction slopes error recovered by DNNCWS.

Fig. 4. （a）Original wavefront；（b）Reconstructed wavefront with DNNCWS；（c）Residual wavefront of DNNCWS；（d）Reconstructed wavefront with GCWS；（e）Residual wavefront of GCWS.

Fig. 5. Comparison of PV and RMS errors of residual wavefront for 30 sets of slopes.（a）PV values of errors；（b）RMS values of errors.

Fig. 6. Comparison of residual wavefront under different compression ratios.（a）Comparison of PV of residual wavefront；（b）Comparison of RMS of residual wavefront.

Fig. 7. （a）Original wavefront；（b）Reconstructed wavefront with DNNCWS；（c）Residual wavefront of DNNCWS；（d）Reconstructed wavefront with GCWS；（e）Residual wavefront of GCWS.

Fig. 8. Wavefront error comparison of 30 sets of slopes.（a）PV of residual wavefront；（b）RMS of residual wavefront.

Fig. 9. Residual wavefront under different magnitude of star.（a）RMS of residual wavefront；（b）PV of residual wavefront.