mlsanityData to 7 Oct 2026

Does Super-Resolution Preserve Defect Evidence? A Low-False-Call Benchmark for Semiconductor Inspection

arXiv 2607.17401 ↗First posted 19 Jul 2026cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

Super-resolution can make inspection images appear sharper without preserving the evidence needed to detect a defect. We study this failure mode with a benchmark that separates reconstruction from detection and evaluates both at a predeclared low false-positive rate. Ten end-to-end repetitions combine independently generated line/space and contact-hole images with model training, calibration, clean controls, weak defects, and a held-out defect morphology. Every reconstruction is scored by the same local residual detector, while direct and jointly trained detectors form a separate comparison track. Reconstruction fidelity and inspection utility diverge: the two learned reconstruction models attain the highest structural similarity yet detect fewer defect pixels than bicubic interpolation in every paired repetition. A direct DeepLabV3 detector reaches 0.1984±0.0385 recall at 0.000174±0.000084 false-positive rate and satisfies the held-out feasibility criterion in all ten repetitions. An illustrative joint model, DPU-WaferSR, passes independent clean calibration but exceeds the held-out limit in all ten repetitions, demonstrating that calibration success does not guarantee transfer. Weak-defect recall remains near zero for every feasible method. Applying the unchanged policies to 4,591 public Carinthia-S masks further reveals large method-dependent shifts on real SEM texture. These results support a simple conclusion: super-resolution for inspection should be judged by preserved task evidence and operating-point transfer, not reconstruction quality alone.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 25 of its 45 references that are in the map. First posted on arXiv 19 Jul 2026.

Builds on

25 of 45 references.

References · 25

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2017On Calibration of Modern Neural Networkscited by 4,860
  3. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  4. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
  5. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  6. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,869
  7. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
  8. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  9. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
  10. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  11. 2021Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Datacited by 961
  12. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
13 more
  1. 2020Pre-Trained Image Processing Transformercited by 891
  2. 2018Residual Dense Network for Image Super-Resolutioncited by 796
  3. 2022Simple Baselines for Image Restorationcited by 701
  4. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  5. 2021Designing a Practical Degradation Model for Deep Blind Image Super-Resolutioncited by 524
  6. 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 452
  7. 2022Activating More Pixels in Image Super-Resolution Transformercited by 433
  8. 2019Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Modelcited by 400
  9. 2018Task-Driven Super Resolution: Object Detection in Low-resolution Imagescited by 52
  10. 2025The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detectioncited by 28
  11. 2024Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Losscited by 17
  12. 2023Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Reviewcited by 2
  13. 2026Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integrationcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.