# HRTEM Denoising on TEMImageNet synthetic (test)

Source: [HRTEM Denoising on TEMImageNet synthetic (test)](<https://www.sota2.com/research/sota/hrtem-denoising-on-temimagenet-synthetic-test>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-20T10:09:01.448Z

Evaluation of High-Resolution Transmission Electron Microscopy (HRTEM) Denoising performance on the test split of the TEMImageNet synthetic dataset, measured using PSNR and SSIM.

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

| Scope | Recorded value |
| --- | --- |
| Task | HRTEM Denoising |
| Dataset (including any stated split) | TEMImageNet synthetic (test) |
| Metric | PSNR |
| Unit | SCALAR |
| Direction | Higher is better |
| Results loaded | 5 of 5 |
| Coverage | All results currently returned for this benchmark |

Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol.

## Metrics

- [PSNR](<https://www.sota2.com/research/sota/hrtem-denoising-on-temimagenet-synthetic-test.md?metric=PSNR>): Higher is better; unit: SCALAR.

- [SSIM](<https://www.sota2.com/research/sota/hrtem-denoising-on-temimagenet-synthetic-test.md?metric=SSIM>): Higher is better; unit: PERCENT.

## Recorded results

| Method | PSNR | Result date | Report paper |
| --- | --- | --- | --- |
| SCGN | 32.55 | Not recorded | 69bcd4a30636e5bf4afc2b32 |
| SFIN | 31.76 | Not recorded | 69bcd4a30636e5bf4afc2b32 |
| HINT | 27.51 | Not recorded | 69bcd4a30636e5bf4afc2b32 |
| AtomSegNet | 22.34 | Not recorded | 69bcd4a30636e5bf4afc2b32 |
| Gaussian | 16.08 | Not recorded | 69bcd4a30636e5bf4afc2b32 |

### SCGN

PSNR: 32.55. Result ID: 69bd1ca247f34ce924a7ca4f.

[Reporting paper](<https://www.sota2.com/research/paper/69bcd4a30636e5bf4afc2b32.md>)

[Method origin paper](<https://www.sota2.com/research/paper/69bcd4a30636e5bf4afc2b32.md>)

Recorded evaluation configuration: {"Model type":"Supervised deep learning","Note":"Training datasets correspond to the testing datasets"}. This record does not assert independent reproduction.

### SFIN

PSNR: 31.76. Result ID: 69bd1ca247f34ce924a7ca4d.

[Reporting paper](<https://www.sota2.com/research/paper/69bcd4a30636e5bf4afc2b32.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa77cd1e209131cea756a1.md>)

Recorded evaluation configuration: {"Model type":"Supervised deep learning","Note":"Training datasets correspond to the testing datasets"}. This record does not assert independent reproduction.

### HINT

PSNR: 27.51. Result ID: 69bd1ca247f34ce924a7ca4e.

[Reporting paper](<https://www.sota2.com/research/paper/69bcd4a30636e5bf4afc2b32.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa7ae11e209131cea85a1a.md>)

Recorded evaluation configuration: {"Model type":"Supervised deep learning","Note":"Training datasets correspond to the testing datasets"}. This record does not assert independent reproduction.

### AtomSegNet

PSNR: 22.34. Result ID: 69bd1ca247f34ce924a7ca4c.

[Reporting paper](<https://www.sota2.com/research/paper/69bcd4a30636e5bf4afc2b32.md>)

Recorded evaluation configuration: {"Model type":"Supervised deep learning","Note":"Training datasets correspond to the testing datasets"}. This record does not assert independent reproduction.

### Gaussian

PSNR: 16.08. Result ID: 69bd1ca247f34ce924a7ca4b.

[Reporting paper](<https://www.sota2.com/research/paper/69bcd4a30636e5bf4afc2b32.md>)

Recorded evaluation configuration: {"Training strategy":"Traditional filter","Note":"Training datasets correspond to the testing datasets"}. This record does not assert independent reproduction.

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