Low light image fusion application
A typical result of taking picture using mobile device at low light condition is a dark image. If night mode available, often it would generate a blurry image. Most mobile applications solve this problem by mean of post-image processing—tuning and editing the dark image after capture. However, this...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2014
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| Online Access: | http://hdl.handle.net/10356/59209 |
| _version_ | 1811688396443090944 |
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| author | Yonas Stephen Suhartono |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Yonas Stephen Suhartono |
| author_sort | Yonas Stephen Suhartono |
| collection | NTU |
| description | A typical result of taking picture using mobile device at low light condition is a dark image. If night mode available, often it would generate a blurry image. Most mobile applications solve this problem by mean of post-image processing—tuning and editing the dark image after capture. However, this method has a major setback; if an area in the image is highly saturated, tuning the image will not enhance the quality. Therefore, this project was aimed to build a low light photography application for iOS platform by mean of multi-exposure image fusion. There is no iOS application till present that uses multi-exposure image fusion technique for low light photography. Fusing the multi-exposure image can obtain the high SNR (signal-to-noise ratio) of a long exposure image and the sharpness of a short exposure image. As the result, pictures taken using this application will be brighter and have a well-balanced noise- to-sharpness ratio. |
| first_indexed | 2024-10-01T05:31:33Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/59209 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:31:33Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | ntu-10356/592092023-03-03T20:58:09Z Low light image fusion application Yonas Stephen Suhartono School of Computer Engineering Ramakrishna Kakarala DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision A typical result of taking picture using mobile device at low light condition is a dark image. If night mode available, often it would generate a blurry image. Most mobile applications solve this problem by mean of post-image processing—tuning and editing the dark image after capture. However, this method has a major setback; if an area in the image is highly saturated, tuning the image will not enhance the quality. Therefore, this project was aimed to build a low light photography application for iOS platform by mean of multi-exposure image fusion. There is no iOS application till present that uses multi-exposure image fusion technique for low light photography. Fusing the multi-exposure image can obtain the high SNR (signal-to-noise ratio) of a long exposure image and the sharpness of a short exposure image. As the result, pictures taken using this application will be brighter and have a well-balanced noise- to-sharpness ratio. Bachelor of Engineering (Computer Science) 2014-04-25T06:07:21Z 2014-04-25T06:07:21Z 2014 2014 Final Year Project (FYP) http://hdl.handle.net/10356/59209 en Nanyang Technological University 76 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Yonas Stephen Suhartono Low light image fusion application |
| title | Low light image fusion application |
| title_full | Low light image fusion application |
| title_fullStr | Low light image fusion application |
| title_full_unstemmed | Low light image fusion application |
| title_short | Low light image fusion application |
| title_sort | low light image fusion application |
| topic | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision |
| url | http://hdl.handle.net/10356/59209 |
| work_keys_str_mv | AT yonasstephensuhartono lowlightimagefusionapplication |