18236635. METHOD AND APPARATUS FOR PROCESSING ARRAY IMAGE simplified abstract (Samsung Electronics Co., Ltd.)
Contents
- 1 METHOD AND APPARATUS FOR PROCESSING ARRAY IMAGE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND APPARATUS FOR PROCESSING ARRAY IMAGE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
METHOD AND APPARATUS FOR PROCESSING ARRAY IMAGE
Organization Name
Inventor(s)
Byong Min Kang of Suwon-si (KR)
Dong Kyung Nam of Suwon-si (KR)
METHOD AND APPARATUS FOR PROCESSING ARRAY IMAGE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18236635 titled 'METHOD AND APPARATUS FOR PROCESSING ARRAY IMAGE
Simplified Explanation
The method and apparatus for array image processing described in the abstract involve receiving sub images from different views of an input array image, generating temporary restored images based on the sub images using a gradient between neighboring pixels, determining matching information based on view differences between pixels of the sub images using a neural network model, extracting refinement targets from matching pairs, refining the matching information through local search, and generating an output image of a single view by merging the temporary restored images based on the refined matching information.
- Sub images are received from different views of an input array image.
- Temporary restored images are generated based on the sub images using a gradient between neighboring pixels.
- Matching information is determined based on view differences between pixels of the sub images using a neural network model.
- Refinement targets are extracted from matching pairs.
- The matching information is refined through local search.
- An output image of a single view is generated by merging the temporary restored images based on the refined matching information.
Potential Applications
This technology could be applied in fields such as medical imaging, surveillance systems, and autonomous vehicles for image processing and enhancement.
Problems Solved
This technology helps in improving the quality and accuracy of images generated from array lenses by refining matching information and merging temporary restored images.
Benefits
The benefits of this technology include enhanced image processing capabilities, improved image quality, and more accurate image reconstruction.
Potential Commercial Applications
The potential commercial applications of this technology could include medical imaging devices, security and surveillance systems, and automotive industry for autonomous vehicles.
Possible Prior Art
One possible prior art for this technology could be image processing algorithms used in medical imaging devices or surveillance systems.
Unanswered Questions
How does this technology compare to existing image processing methods?
This article does not provide a direct comparison between this technology and existing image processing methods. Further research and analysis would be needed to determine the specific advantages and disadvantages of this technology in comparison to others.
What are the limitations of this technology in real-world applications?
The article does not address the potential limitations of implementing this technology in real-world applications. It would be important to consider factors such as computational resources, processing time, and scalability when evaluating the practicality of this technology.
Original Abstract Submitted
A method and apparatus for array image processing are provided. The method includes receiving sub images corresponding to different views of an input array image generated through an array lens, generating temporary restored images based on the sub images using a gradient between neighboring pixels of each of the sub images, determining matching information based on a view difference between pixels of the sub images using a neural network model, based on a pixel distance between matching pairs of the pixels of the sub images using the matching information, extracting refinement targets from the matching pairs, refining the matching information by replacing at least some of target pixels included in the refinement targets based on a local search of a region based on pixel locations of the refinement targets, and generating an output image of a single view by merging the temporary restored images based on the refined matching information.
