Image degradation arises from various environmental conditions due to the exis tence of aerosols such as fog, haze, and dust. These phenomena mitigate image vis ibility by creating color distortion, reducing contrast, and fainting object surfaces.
Although the end-to-end deep learning approach has made significant progress in
the field of homogeneous dehazing, the image quality of these algorithms in the
context of non-homogeneous real-world images has not yet been satisfactory. We
argue two main reasons that are responsible for the problem: 1) First, due to the
unbalanced information processing of the high-level and low-level information in
conventional dehazing algorithms, 2) due to lack of trainable data pairs. To ad dress the above two problems, we propose a parallel dual-branch design that aims
to balance the processing of high-level and low-level information, and through a
method of transfer learning, utilize the small data sets to their full potential. The
results from the two parallel branches are aggregated in a simple fusion tail, in
which the high-level and low-level information are fused, and the final result is
generated. To demonstrate the effectiveness of our proposed method, we present
extensive experimental results in the thesis. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27005 |
| Date | January 2021 |
| Creators | Song, Xiang |
| Contributors | Jun, Chen, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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