Image dehazing based on double branch convolution and detail enhancement
Because of detail loss, color distortion and contrast reduction in the image dehazing process in a haze condition, we proposed the image dehazing network based on double branch convolution and detail enhancement, which consists of image dehazing module and detail enhancement module. First, in the im...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
EDP Sciences
2025-02-01
|
| Series: | Xibei Gongye Daxue Xuebao |
| Subjects: | |
| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2025/01/jnwpu2025431p109/jnwpu2025431p109.html |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850269547030904832 |
|---|---|
| author | ZHAI Fengwen ZHU Yutong JIN Jing |
| author_facet | ZHAI Fengwen ZHU Yutong JIN Jing |
| author_sort | ZHAI Fengwen |
| collection | DOAJ |
| description | Because of detail loss, color distortion and contrast reduction in the image dehazing process in a haze condition, we proposed the image dehazing network based on double branch convolution and detail enhancement, which consists of image dehazing module and detail enhancement module. First, in the image dehazing module, we designed a double branch convolutional block based on depth-separable convolution and differential convolution and then combined it with the U-Net network, effectively reducing the detail loss in the image dehazing process. In the image dehazing model, we introduced the attention module composed of channel attention mechanism and pixel attention mechanism, which improves its feature extraction ability, suppresses the features that are not related to the current task and further reduces the color distortion and contrast in the image dehazing process. Then, we input the image dehazed with the image dehazing module into the detail enhancement module to further recover image details, so that the image is more similar to that in its real domain. The combination of the image dehazing module with the detail enhancement module improves the generalization ability of the image dehazing network and makes it more adaptable to the haze dataset. We carried out experiments with the public datasets ITS and Haze4K and the public real dataset IHAZE. The quantitative objective analysis and comparison show that the peak signal-to-noise ratio and the structural similarity index reach 39.69 dB and 0.994 respectively, indicating that there is a certain improvement compared with the optimal algorithm in the comparison network model. The subjective visual analysis shows that the image dehazed with the image dehazing network we proposed is more similar to the real no-haze image in terms of detail, color, contrast and so on. |
| format | Article |
| id | doaj-art-cdf442d2f8be410c9d895370abe0979b |
| institution | OA Journals |
| issn | 1000-2758 2609-7125 |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Xibei Gongye Daxue Xuebao |
| spelling | doaj-art-cdf442d2f8be410c9d895370abe0979b2025-08-20T01:53:06ZzhoEDP SciencesXibei Gongye Daxue Xuebao1000-27582609-71252025-02-0143110911810.1051/jnwpu/20254310109jnwpu2025431p109Image dehazing based on double branch convolution and detail enhancementZHAI Fengwen0ZHU Yutong1JIN Jing2School of Electronic and Information Engineering, Lanzhou Jiaotong UniversitySchool of Electronic and Information Engineering, Lanzhou Jiaotong UniversitySchool of Electronic and Information Engineering, Lanzhou Jiaotong UniversityBecause of detail loss, color distortion and contrast reduction in the image dehazing process in a haze condition, we proposed the image dehazing network based on double branch convolution and detail enhancement, which consists of image dehazing module and detail enhancement module. First, in the image dehazing module, we designed a double branch convolutional block based on depth-separable convolution and differential convolution and then combined it with the U-Net network, effectively reducing the detail loss in the image dehazing process. In the image dehazing model, we introduced the attention module composed of channel attention mechanism and pixel attention mechanism, which improves its feature extraction ability, suppresses the features that are not related to the current task and further reduces the color distortion and contrast in the image dehazing process. Then, we input the image dehazed with the image dehazing module into the detail enhancement module to further recover image details, so that the image is more similar to that in its real domain. The combination of the image dehazing module with the detail enhancement module improves the generalization ability of the image dehazing network and makes it more adaptable to the haze dataset. We carried out experiments with the public datasets ITS and Haze4K and the public real dataset IHAZE. The quantitative objective analysis and comparison show that the peak signal-to-noise ratio and the structural similarity index reach 39.69 dB and 0.994 respectively, indicating that there is a certain improvement compared with the optimal algorithm in the comparison network model. The subjective visual analysis shows that the image dehazed with the image dehazing network we proposed is more similar to the real no-haze image in terms of detail, color, contrast and so on.https://www.jnwpu.org/articles/jnwpu/full_html/2025/01/jnwpu2025431p109/jnwpu2025431p109.htmldifference convolutiondepth-separable convolutionimage dehazingattention mechanism |
| spellingShingle | ZHAI Fengwen ZHU Yutong JIN Jing Image dehazing based on double branch convolution and detail enhancement Xibei Gongye Daxue Xuebao difference convolution depth-separable convolution image dehazing attention mechanism |
| title | Image dehazing based on double branch convolution and detail enhancement |
| title_full | Image dehazing based on double branch convolution and detail enhancement |
| title_fullStr | Image dehazing based on double branch convolution and detail enhancement |
| title_full_unstemmed | Image dehazing based on double branch convolution and detail enhancement |
| title_short | Image dehazing based on double branch convolution and detail enhancement |
| title_sort | image dehazing based on double branch convolution and detail enhancement |
| topic | difference convolution depth-separable convolution image dehazing attention mechanism |
| url | https://www.jnwpu.org/articles/jnwpu/full_html/2025/01/jnwpu2025431p109/jnwpu2025431p109.html |
| work_keys_str_mv | AT zhaifengwen imagedehazingbasedondoublebranchconvolutionanddetailenhancement AT zhuyutong imagedehazingbasedondoublebranchconvolutionanddetailenhancement AT jinjing imagedehazingbasedondoublebranchconvolutionanddetailenhancement |