Fast and Noise Robust Depth from Focus using Ring Difference Filter with Your Mobile Phone
발표자: 서재흥 (KIST 연구원) 발표일: 17.9. He is currently a researcher at the Center for Human-centered Interaction for Coexistence (CHIC), located in KIST. 개요: Depth from focus (DfF) is a method of estimating depth of a scene by using the information acquired through the change of the focus of a camera. Within the framework of DfF, the focus measure (FM) forms the foundation on which the accuracy of the output is determined. With the result from the FM, the role of a DfF pipeline is to determine and recalculate unreliable measurements while enhancing those that are reliable. In this paper, we propose a new FM that more accurately and robustly measures focus, which we call the "ring difference filter" (RDF). FMs can usually be categorized as confident local methods or noise robust non-local methods. RDF’s unique ring-and-disk structure allows it to have the advantageous sides of both local and non-local FMs. We then describe an efficient pipeline that utilizes the properties that the RDF brings. Our method is able to reproduce results that are on par with or even better than those of the state-of-the-art, while spending less time in computation.
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Fast and Noise Robust Depth from Focus using Ring Difference Filter with Your Mobile Phone
- 1. Noise Robust Depth from Focus using a Ring Difference Filter Jaeheung Surh Hae-Gon Jeon Yunwon Park Sunghoon Im Hyowon Ha In So Kweon KAIST CVPR 2017
- 3. Stereo Cost Breakdown Camera : 10% of total component costs! Camera
- 4. Single Camera Depth Device moved too quickly Subject lost or moved This scene cannot be captured Depthmap Dense Point CloudInput Image Sequence Google camera App. - Unstable! Structure from Small Motion - Large computation time! [1] Im et al., High Quality Structure from Small Motion for Rolling Shutter Cameras, ICCV 2015
- 5. Previous Works Input Suwajanakorn et al. [2] [2] [3] [2] Suwajanakorn et al., Depth from Focus with Your Mobile Phone, CVPR 2015 [3] Kim et al., The Method for Defocusing Selfie Taken by Mobile Frontal Camera Using Burst Shot, ICASSP 2016
- 6. Objective 1. Lower the computational complexity 2. Work with independence to camera type 3. Robust to environmental constraints
- 7. Contributions 1. Ring difference Filter (RDF) • Robust focus measure with high confidence and synergy with cost aggregation 2. Efficient Depth from Focus Pipeline • Propose a new depth from focus pipeline that does not use heavy optimization. • Propose a new cost aggregation method that is robust to noise using RDF.
- 8. Input Focal Stack Captured using an LG V10 smartphone
- 9. Input Focal Stack High ISO → High Noise Noise Level
- 11. State-of-the-Art Supasorn et al. CVPR 2015 Focal Stack Alignment Focus Measure Graph Cuts Joint Optimization Depth Refinement ~ 20 minutes
- 12. Proposed Focal Stack Alignment Focus Measure Cost Aggregation Depth Refinement 20 minutes → 6 seconds (200x speedup)
- 13. Focus Measures (FM) and the Proposed Ring Difference Filter (RDF)
- 14. Focus Measures (FM) • Focus measure measures how much the image is in focus • How much the focal plane is on the image plane (CCD) • Various methods: • Gradient-based • Laplacian-based • Wavelet-based • DCT-based • Statistics-based
- 15. Proposed Focus Measure (FM) 0 1 1 1 0 1 0 0 0 1 1 0 -12 0 1 1 0 0 0 1 0 1 1 1 0 1 4 1 4 -20 4 1 4 1 Ring Difference Filter (RDF) Laplacian
- 16. 1 4 1 4 -20 4 1 4 1 Conventional FM Laplacian
- 17. Toy FM Naïve Filter 0 1 1 1 0 1 1 1 1 1 1 1 -20 1 1 1 1 1 1 1 0 1 1 1 0
- 18. 1 0 4 0 1 0 0 0 0 0 4 0 -20 0 4 0 0 0 0 0 1 0 4 0 1 Toy FM Resized Laplacian
- 19. Proposed FM (RDF) 0 1 1 1 0 1 0 0 0 1 1 0 -12 0 1 1 0 0 0 1 0 1 1 1 0 Ring Difference Filter (RDF)
- 21. Comparison with Top Performing FM Modified Laplacian Wavelet Eigen Value RDF σ = 0 σ = 0.005 σ = 0.01 σ = 0.02
- 22. RDF-based Depth from Focus Pipeline
- 23. Initial Depth
- 24. Aggregated Depth
- 25. Bokeh Mask
- 26. Median of Absolute Deviation (MAD) Mask
- 27. Bokeh + MAD Mask
- 28. Aggregated Depth with Mask
- 31. Comparison with State-of-the-Art Supasorn et al.All-in-Focus Image Proposed
- 32. Comparison with State-of-the-Art Supasorn et al.All-in-Focus Image Proposed
- 33. Other Examples
- 34. Other Examples
- 35. Stylization Example All-in-focus Image Stylization Results
- 36. Thank You for Listening! Any Questions? (You can ask me in Korean) For the paper and code (soon-ish): jaeheungs.github.io