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Fast Direct Super-Resolution by Simple Functions

Fast Direct Super-Resolution by Simple Functions. Chih -Yuan Yang and Ming- Hsuan Yang 12/14/13. Outline. Introduction Related work Proposed method Experimental results Conclusions. Introduction. Super-Resolution = Predicting pixel intensities From single or multiple image(s)

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Fast Direct Super-Resolution by Simple Functions

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  1. Fast Direct Super-Resolution by Simple Functions Chih-Yuan Yang and Ming-Hsuan Yang 12/14/13

  2. Outline • Introduction • Related work • Proposed method • Experimental results • Conclusions

  3. Introduction • Super-Resolution = Predicting pixel intensities • From single or multiple image(s) • For spatial or temporal intestines 480 120 320 80

  4. Challenges • Effectiveness • Numerous pixel intensities • Stability • Various image content • Computational load

  5. Related Work - Bicubic Interpolation • Predicting intensities by interpolating nearbypixels • Simple and fast • But over-smooth

  6. Self-Similar Exemplars [Glasner 11] I4 • Predict HR patches through example patches found in a self-generated image pyramid • Sharp and clear edges • Blurred textures • Slow I3 I2 I1 I0 I-1 I-2 I-3

  7. Gradient Profile Prior [Sun08] • Predict intensities by a edge model • Sharp edges, but jaggy • Only effective for edges

  8. Sparse Representation [Yang08] • Predicting HR patch features by learned sparse dictionaries • Rich details • Noise artifacts along edges

  9. Proposed Method • Split LR feature space for effectiveness • Use simple features and simple functions for speed • Exploit a large image set to collect training samples • Asymmetric computational load • slow for training • fast for test

  10. Training Phase (1)Generate LR images • Generate LR images from HR training images • Extract patch pairs in LR and HR • Since there is a convolution for HR images, we cropLR patches affected by predicted HR pixels (blue region)

  11. Training Phase (2)Extract LR/HR features • Features: • LR: LR intensity minus the mean of the LR patches • HR: HR intensity minus the mean of the LR patches • High-frequency information • Cluster the LR training features by K-mean • split the LR feature space into K subspaces

  12. Cluster Centers and Patch Numbers

  13. Training Phase (3)Collect training instances • Randomly select sufficeint LR/HR feature pairs for each cluster • For some cluster centers, use bicubic interpolation if no sufficient instances available

  14. Training Phase (4)Learn regression coefficients • C: coefficients • W: HR features (1000 instances) • V: LR features (1000 instances)

  15. Test Phase • Crop LR patches • Compute patch mean, and extract LR features • Find the closest cluster center • Compute HR features • HR patch intensity = HR feature + LR patch mean • Average overlapped HR patches as output

  16. Experimental Results - Child

  17. Experimental Results - Lena

  18. Experimental Results - IC • No groundtruth image

  19. Experimental Results - Mansion

  20. Experimental Results - Mermaid

  21. Experimental Results - Shore

  22. Experiments on BSD200 dataset

  23. Performance (averaged) and Execution Time for BSD200 Dataset

  24. Conclusions • Clear and sharp edges • Rich texture details • Easy for implementation • Fast to generate SR images

  25. Insight • Split of the LR feature space • K has to be large enough • Exploit a large image set to collect sufficient examples for high-frequency patches • Regression methods make little difference

  26. Code and Dataset Available • https://eng.ucmerced.edu/people/cyang35 • Including code for training phase

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