Detection and Tracking Scheme for Line Scratch Removal in an Image Sequence 1st edition by Bernard Besserer, Cedric Thire ISBN 3540219828 9783540219828

Detection and Tracking Scheme for Line Scratch Removal in an Image Sequence 1st edition by Bernard Besserer, Cedric Thiré – Ebook PDF Instant Download/Delivery. 3540219828, 978-3540219828
Full download Detection and Tracking Scheme for Line Scratch Removal in an Image Sequence 1st Edition after payment

Product details:

ISBN 10:       3540219828
ISBN 13: 978-3540219828
Author:        Bernard Besserer, Cedric Thiré 
 

A detection and tracking approach is proposed for line scratch removal in a digital film restoration process. Unlike random impulsive distortions such as dirt spots, line scratch artifacts persist across several frames. Hence, motion compensated methods will fail, as well as single-frame methods if scratches are unsteady or fragmented.

The proposed method uses as input projections of each image of the input sequence. First, a 1D-extrema detector provides candidates. Next, a MHT (Multiple Hypothesis Tracker) uses these candidates to create and keep multiple hypothesis. As the tracking goes further through the sequence, each hypothesis gains or looses evidence. To avoid a combinatorial explosion, the hypothesis tree is sequentially pruned, preserving a list of the best ones. An energy function (quality of the candidates, comparison to a model) is used for the path hypothesis sorting. As hypothesis are set up at each iteration, even if no information is available, a tracked path might cross gaps (missed detection or speckled scratches).

At last, the tracking stage feeds the correction process. Since this contribution focus on the detection stage, only tracking results are given.

Detection and Tracking Scheme for Line Scratch Removal in an Image Sequence 1st Table of contents:

• Introduction

  • 1.1 Background and Motivation
  • 1.2 The Problem of Line Scratches in Image Sequences
  • 1.3 Importance of Scratch Removal in Image and Video Restoration
  • 1.4 Objectives of the Paper
  • 1.5 Structure of the Paper

• Related Work

  • 2.1 Image Restoration and Repair Techniques
  • 2.2 Scratch Detection Methods in Static and Dynamic Images
  • 2.3 Tracking Techniques for Artifacts in Image Sequences
  • 2.4 Approaches for Removing Artifacts in Video Sequences
  • 2.5 Limitations of Existing Scratch Removal Methods

• Problem Formulation

  • 3.1 Definition of Line Scratches in Image Sequences
  • 3.2 Challenges in Detecting Line Scratches (e.g., noise, occlusions)
  • 3.3 Importance of Temporal Consistency in Scratch Tracking
  • 3.4 Assumptions and Constraints in the Proposed Method

• Scratch Detection in Image Sequences

  • 4.1 Image Preprocessing for Scratch Detection
  • 4.2 Feature Extraction for Scratch Identification
  • 4.3 Edge Detection and Line Tracking Algorithms
  • 4.4 Spatial and Temporal Scratch Detection Strategies
  • 4.5 Adaptive Methods for Identifying Different Types of Scratches

• Tracking Line Scratches Across Frames

  • 5.1 Temporal Tracking of Scratches in Image Sequences
  • 5.2 Optical Flow and Motion Tracking for Scratch Localization
  • 5.3 Feature-Based Tracking Techniques (e.g., corner detection, SURF)
  • 5.4 Matching Scratches Across Frames
  • 5.5 Handling False Positives and False Negatives in Tracking

• Line Scratch Removal Techniques

  • 6.1 Inpainting Methods for Scratch Removal
  • 6.2 Context-Aware Patch Propagation and Texture Synthesis
  • 6.3 Image Restoration Using Image Statistics and Neighboring Information
  • 6.4 Combining Temporal and Spatial Information for Scratch Repair
  • 6.5 Post-Processing for Improving Scratch Removal Quality

• Algorithm Design and Implementation

  • 7.1 Overview of the Scratch Detection and Tracking Pipeline
  • 7.2 Scratch Detection and Tracking Algorithm Steps
  • 7.3 Implementation Details (Software, Tools, and Libraries)
  • 7.4 Real-Time Processing Considerations
  • 7.5 Efficiency and Scalability of the Algorithm

• Experimental Setup and Evaluation

  • 8.1 Datasets for Scratch Detection and Removal
  • 8.2 Evaluation Metrics for Scratch Detection Accuracy
  • 8.3 Quantitative Evaluation of Scratch Removal Quality
  • 8.4 Comparative Analysis with Other Scratch Removal Methods
  • 8.5 Robustness Evaluation in Noisy and Challenging Scenarios

• Results and Discussion

  • 9.1 Qualitative Results of Scratch Detection and Removal
  • 9.2 Performance Comparison Across Different Image Sequences
  • 9.3 Analysis of Tracking Accuracy and Scratch Repair Quality
  • 9.4 Impact of Scratch Size, Duration, and Motion on Results
  • 9.5 Discussion on the Limitations and Trade-offs in the Proposed Method

• Applications of Scratch Removal in Image and Video Restoration

  • 10.1 Restoration of Old Films and Archival Footage
  • 10.2 Repairing Scratches in Medical Imaging and Surveillance
  • 10.3 Use in Video Editing and Post-production
  • 10.4 Enhancing Visual Quality in Forensic and Security Applications
  • 10.5 Future Use Cases in Real-Time Video Processing

• Challenges and Future Directions

  • 11.1 Improving Accuracy in Scratch Detection under Challenging Conditions
  • 11.2 Real-Time Scratch Removal in Video Streams
  • 11.3 Addressing Larger and Complex Scratches in Image Sequences
  • 11.4 Integration of Deep Learning for Enhanced Scratch Detection and Repair
  • 11.5 Future Research Directions and Open Problems

• Conclusion

  • 12.1 Summary of Contributions and Findings
  • 12.2 Practical Implications for Image and Video Restoration
  • 12.3 Future Prospects for Scratch Removal Methods
  • 12.4 Final Remarks

People also search for Detection and Tracking Scheme for Line Scratch Removal in an Image Sequence 1st:

detection and tracking meet drones challenge
detection and tracking of moving objects hidden from view
a technology typically used for tracking objects
testing and tracking system cats
feature detection vs signal detection