Image Reconstruction on CytoImageNet (test)
31.42PSNRSTE–WCRR
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| STE–WCRRNumber of patterns (M)=2048, Sampling Ratio=12.5%, Binarization Strategy=STE, Regularizer=WCRR2025.08 | 31.42 | 0.812 | |
| STE–TDVNumber of patterns (M)=2048, Sampling Ratio=12.5%, Binarization Strategy=STE, Regularizer=TDV2025.08 | 31.22 | 0.806 | |
| RnP–WCRRNumber of patterns (M)=2048, Sampling Ratio=12.5%, Binarization Strategy=RnP, Regularizer=WCRR2025.08 | 30.8 | 0.793 | |
| STE–TDVNumber of patterns (M)=1024, Sampling Ratio=6.3%, Binarization Strategy=STE, Regularizer=TDV2025.08 | 30.51 | 0.783 | |
| RnP–TDVNumber of patterns (M)=2048, Sampling Ratio=12.5%, Binarization Strategy=RnP, Regularizer=TDV2025.08 | 30.49 | 0.783 | |
| SH–TVNumber of patterns (M)=2048, Sampling Ratio=12.5%, Pattern Design=Scrambled Hadamard (SH), Regularizer=Total Variation (TV)2025.08 | 30.43 | 0.782 | |
| STE–WCRRNumber of patterns (M)=1024, Sampling Ratio=6.3%, Binarization Strategy=STE, Regularizer=WCRR2025.08 | 30.41 | 0.778 | |
| Gaussian–TVNumber of patterns (M)=2048, Sampling Ratio=12.5%, Pattern Design=Fixed Gaussian, Regularizer=Total Variation (TV)2025.08 | 30.3 | 0.778 | |
| SH–TDVNumber of patterns (M)=2048, Sampling Ratio=12.5%, Pattern Design=Scrambled Hadamard (SH), Regularizer=TDV2025.08 | 29.72 | 0.764 | |
| Gaussian–TDVNumber of patterns (M)=2048, Sampling Ratio=12.5%, Pattern Design=Fixed Gaussian, Regularizer=TDV2025.08 | 29.61 | 0.76 | |
| SH–WCRRNumber of patterns (M)=2048, Sampling Ratio=12.5%, Pattern Design=Scrambled Hadamard (SH), Regularizer=WCRR2025.08 | 29.44 | 0.756 | |
| RnP–WCRRNumber of patterns (M)=1024, Sampling Ratio=6.3%, Binarization Strategy=RnP, Regularizer=WCRR2025.08 | 29.42 | 0.748 | |
| Gaussian–WCRRNumber of patterns (M)=2048, Sampling Ratio=12.5%, Pattern Design=Fixed Gaussian, Regularizer=WCRR2025.08 | 29.37 | 0.754 | |
| RnP–TDVNumber of patterns (M)=1024, Sampling Ratio=6.3%, Binarization Strategy=RnP, Regularizer=TDV2025.08 | 29.2 | 0.743 | |
| STE–TDVNumber of patterns (M)=512, Sampling Ratio=3.1%, Binarization Strategy=STE, Regularizer=TDV2025.08 | 29.1 | 0.736 | |
| STE–WCRRNumber of patterns (M)=512, Sampling Ratio=3.1%, Binarization Strategy=STE, Regularizer=WCRR2025.08 | 29.02 | 0.732 | |
| RnP–WCRRNumber of patterns (M)=512, Sampling Ratio=3.1%, Binarization Strategy=RnP, Regularizer=WCRR2025.08 | 28.17 | 0.706 | |
| RnP–TDVNumber of patterns (M)=512, Sampling Ratio=3.1%, Binarization Strategy=RnP, Regularizer=TDV2025.08 | 28 | 0.701 | |
| SH–TDVNumber of patterns (M)=1024, Sampling Ratio=6.3%, Pattern Design=Scrambled Hadamard (SH), Regularizer=TDV2025.08 | 27.97 | 0.707 | |
| Gaussian–TDVNumber of patterns (M)=1024, Sampling Ratio=6.3%, Pattern Design=Fixed Gaussian, Regularizer=TDV2025.08 | 27.9 | 0.704 | |
| SH–WCRRNumber of patterns (M)=1024, Sampling Ratio=6.3%, Pattern Design=Scrambled Hadamard (SH), Regularizer=WCRR2025.08 | 27.71 | 0.7 | |
| Gaussian–WCRRNumber of patterns (M)=1024, Sampling Ratio=6.3%, Pattern Design=Fixed Gaussian, Regularizer=WCRR2025.08 | 27.64 | 0.698 | |
| SH–TVNumber of patterns (M)=1024, Sampling Ratio=6.3%, Pattern Design=Scrambled Hadamard (SH), Regularizer=Total Variation (TV)2025.08 | 27.6 | 0.689 | |
| Gaussian–TVNumber of patterns (M)=1024, Sampling Ratio=6.3%, Pattern Design=Fixed Gaussian, Regularizer=Total Variation (TV)2025.08 | 27.52 | 0.691 | |
| STE–TDVNumber of patterns (M)=256, Sampling Ratio=1.6%, Binarization Strategy=STE, Regularizer=TDV2025.08 | 27.44 | 0.682 | |
| STE–WCRRNumber of patterns (M)=256, Sampling Ratio=1.6%, Binarization Strategy=STE, Regularizer=WCRR2025.08 | 27.36 | 0.678 | |
| RnP–TDVNumber of patterns (M)=256, Sampling Ratio=1.6%, Binarization Strategy=RnP, Regularizer=TDV2025.08 | 26.88 | 0.666 | |
| RnP–WCRRNumber of patterns (M)=256, Sampling Ratio=1.6%, Binarization Strategy=RnP, Regularizer=WCRR2025.08 | 26.82 | 0.663 | |
| SH–TDVNumber of patterns (M)=512, Sampling Ratio=3.1%, Pattern Design=Scrambled Hadamard (SH), Regularizer=TDV2025.08 | 26.07 | 0.647 | |
| Gaussian–TDVNumber of patterns (M)=512, Sampling Ratio=3.1%, Pattern Design=Fixed Gaussian, Regularizer=TDV2025.08 | 26.02 | 0.643 | |
| Gaussian–WCRRNumber of patterns (M)=512, Sampling Ratio=3.1%, Pattern Design=Fixed Gaussian, Regularizer=WCRR2025.08 | 25.77 | 0.64 | |
| SH–WCRRNumber of patterns (M)=512, Sampling Ratio=3.1%, Pattern Design=Scrambled Hadamard (SH), Regularizer=WCRR2025.08 | 25.77 | 0.64 | |
| STE–TDVNumber of patterns (M)=128, Sampling Ratio=0.8%, Binarization Strategy=STE, Regularizer=TDV2025.08 | 25.43 | 0.625 | |
| RnP–TDVNumber of patterns (M)=128, Sampling Ratio=0.8%, Binarization Strategy=RnP, Regularizer=TDV2025.08 | 25.3 | 0.618 | |
| STE–WCRRNumber of patterns (M)=128, Sampling Ratio=0.8%, Binarization Strategy=STE, Regularizer=WCRR2025.08 | 24.98 | 0.611 | |
| RnP–WCRRNumber of patterns (M)=128, Sampling Ratio=0.8%, Binarization Strategy=RnP, Regularizer=WCRR2025.08 | 24.97 | 0.611 | |
| SH–TVNumber of patterns (M)=512, Sampling Ratio=3.1%, Pattern Design=Scrambled Hadamard (SH), Regularizer=Total Variation (TV)2025.08 | 24.85 | 0.601 | |
| Gaussian–TVNumber of patterns (M)=512, Sampling Ratio=3.1%, Pattern Design=Fixed Gaussian, Regularizer=Total Variation (TV)2025.08 | 24.81 | 0.606 | |
| SH–TDVNumber of patterns (M)=256, Sampling Ratio=1.6%, Pattern Design=Scrambled Hadamard (SH), Regularizer=TDV2025.08 | 24.02 | 0.585 | |
| Gaussian–TDVNumber of patterns (M)=256, Sampling Ratio=1.6%, Pattern Design=Fixed Gaussian, Regularizer=TDV2025.08 | 23.94 | 0.581 | |
| SH–WCRRNumber of patterns (M)=256, Sampling Ratio=1.6%, Pattern Design=Scrambled Hadamard (SH), Regularizer=WCRR2025.08 | 23.04 | 0.565 | |
| DCANNumber of patterns (M)=512, Sampling Ratio=3.1%, Type=End-to-End Deep Learning2025.08 | 22.73 | 0.516 | |
| DCANNumber of patterns (M)=2048, Sampling Ratio=12.5%, Type=End-to-End Deep Learning2025.08 | 22.73 | 0.511 | |
| DCANNumber of patterns (M)=1024, Sampling Ratio=6.3%, Type=End-to-End Deep Learning2025.08 | 22.66 | 0.516 | |
| Gaussian–WCRRNumber of patterns (M)=256, Sampling Ratio=1.6%, Pattern Design=Fixed Gaussian, Regularizer=WCRR2025.08 | 22.59 | 0.547 | |
| DCANNumber of patterns (M)=256, Sampling Ratio=1.6%, Type=End-to-End Deep Learning2025.08 | 22.42 | 0.509 | |
| DCANNumber of patterns (M)=128, Sampling Ratio=0.8%, Type=End-to-End Deep Learning2025.08 | 22.4 | 0.513 | |
| SH–TVNumber of patterns (M)=256, Sampling Ratio=1.6%, Pattern Design=Scrambled Hadamard (SH), Regularizer=Total Variation (TV)2025.08 | 22.29 | 0.523 | |
| Gaussian–TVNumber of patterns (M)=256, Sampling Ratio=1.6%, Pattern Design=Fixed Gaussian, Regularizer=Total Variation (TV)2025.08 | 22.14 | 0.528 | |
| SH–TDVNumber of patterns (M)=128, Sampling Ratio=0.8%, Pattern Design=Scrambled Hadamard (SH), Regularizer=TDV2025.08 | 21.61 | 0.514 | |
| Gaussian–TDVNumber of patterns (M)=128, Sampling Ratio=0.8%, Pattern Design=Fixed Gaussian, Regularizer=TDV2025.08 | 21.09 | 0.501 | |
| SH–TVNumber of patterns (M)=128, Sampling Ratio=0.8%, Pattern Design=Scrambled Hadamard (SH), Regularizer=Total Variation (TV)2025.08 | 19.99 | 0.457 | |
| Gaussian–TVNumber of patterns (M)=128, Sampling Ratio=0.8%, Pattern Design=Fixed Gaussian, Regularizer=Total Variation (TV)2025.08 | 19.95 | 0.464 | |
| SH–WCRRNumber of patterns (M)=128, Sampling Ratio=0.8%, Pattern Design=Scrambled Hadamard (SH), Regularizer=WCRR2025.08 | 19.19 | 0.461 | |
| Gaussian–WCRRNumber of patterns (M)=128, Sampling Ratio=0.8%, Pattern Design=Fixed Gaussian, Regularizer=WCRR2025.08 | 19.17 | 0.461 |