Investigating nonlinear point processing in image enhancement
Today, the use of convolutional neural network models for deep learning in the field of image processing has become a general trend. However, to obtain satisfying performance, these networks usually stack a dozen of layers or require multiple iterations, which not only increase their complexity but...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/158487 |
| _version_ | 1826125775705735168 |
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| author | Duan, Shengan |
| author2 | Jiang Xudong |
| author_facet | Jiang Xudong Duan, Shengan |
| author_sort | Duan, Shengan |
| collection | NTU |
| description | Today, the use of convolutional neural network models for deep learning in the field of image processing has become a general trend. However, to obtain satisfying performance, these networks usually stack a dozen of layers or require multiple iterations, which not only increase their complexity but also weaken their interpretability. The reason behind such a phenomenon is that, the key transformations in current networks are mainly realized by modules with limited nonlinearity, such as convolutions plus ReLUs.
Some simple nonlinear processing like point processing is highly effective for image enhancement for our visual perception, and histogram equalization is one of the most common methods among them. By studying the principles of the traditional method of histogram equalization and introducing it into CNNs, it may be possible to improve the performance of the latter.
In this report, we will first understand the principles and functions of several commonly used image point processing methods. Then, we'll dive into several different histogram equalizations and use them in MATLAB. Finally, we will build a CNN to compensate the results of HE to the desired output. We will see how the image pattern extraction capability of CNN can help HE images to be closer to the target image, and what are the advantages and disadvantages of this approach. |
| first_indexed | 2024-10-01T06:41:54Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/158487 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:41:54Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1584872023-07-07T18:56:29Z Investigating nonlinear point processing in image enhancement Duan, Shengan Jiang Xudong School of Electrical and Electronic Engineering EXDJiang@ntu.edu.sg Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Today, the use of convolutional neural network models for deep learning in the field of image processing has become a general trend. However, to obtain satisfying performance, these networks usually stack a dozen of layers or require multiple iterations, which not only increase their complexity but also weaken their interpretability. The reason behind such a phenomenon is that, the key transformations in current networks are mainly realized by modules with limited nonlinearity, such as convolutions plus ReLUs. Some simple nonlinear processing like point processing is highly effective for image enhancement for our visual perception, and histogram equalization is one of the most common methods among them. By studying the principles of the traditional method of histogram equalization and introducing it into CNNs, it may be possible to improve the performance of the latter. In this report, we will first understand the principles and functions of several commonly used image point processing methods. Then, we'll dive into several different histogram equalizations and use them in MATLAB. Finally, we will build a CNN to compensate the results of HE to the desired output. We will see how the image pattern extraction capability of CNN can help HE images to be closer to the target image, and what are the advantages and disadvantages of this approach. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-06-03T06:34:05Z 2022-06-03T06:34:05Z 2022 Final Year Project (FYP) Duan, S. (2022). Investigating nonlinear point processing in image enhancement. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158487 https://hdl.handle.net/10356/158487 en W3347-212 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Duan, Shengan Investigating nonlinear point processing in image enhancement |
| title | Investigating nonlinear point processing in image enhancement |
| title_full | Investigating nonlinear point processing in image enhancement |
| title_fullStr | Investigating nonlinear point processing in image enhancement |
| title_full_unstemmed | Investigating nonlinear point processing in image enhancement |
| title_short | Investigating nonlinear point processing in image enhancement |
| title_sort | investigating nonlinear point processing in image enhancement |
| topic | Engineering::Electrical and electronic engineering::Electronic systems::Signal processing |
| url | https://hdl.handle.net/10356/158487 |
| work_keys_str_mv | AT duanshengan investigatingnonlinearpointprocessinginimageenhancement |