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MF-MSCNN: Multi-Feature based Multi-Scale Convolutional Neural Network for Image Dehazing via Input Transformation (Record no. 133159)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01758nam a2200145 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 251101b |||||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Balla Pavan Kumar |
| 245 ## - TITLE STATEMENT | |
| Title | MF-MSCNN: Multi-Feature based Multi-Scale Convolutional Neural Network for Image Dehazing via Input Transformation |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p1547-1559 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. biblio.abstract | Recently, many deep learning algorithms have been proposed for image dehazing. However, in most of these techniques, the issues of under-exposure and over-saturation are observed. These problems appear because of inadequate consideration of the overall haze level, i.e. Less-Haze (LH), Medium-Haze (MH), and High-Haze (HH), of hazy images while dehazing. Therefore, a Multi-Feature-based Multi-Scale Convolutional Neural Network (MF-MSCNN) is proposed, which considers the haze density of hazy images as a parameter while dehazing. Firstly, the classification operation is performed to categorize the hazy images into LH, MH, and HH. Based on this categorization, a haze density map is generated, which is concatenated to the input hazy image as part of input transformation (IT). Subsequently, the LH, MH, and HH features are extracted using the proposed MF-MSCNN. These features are adaptively chosen by the pooling layer to obtain an efficient transmission map from which the dehazed image is retrieved. The proposed work using IT operation and the MF-MSCNN model produces better results for all categories of hazy images when compared to the existing methods |
| 654 ## - SUBJECT ADDED ENTRY--FACETED TOPICAL TERMS | |
| Subject | <a href="Image dehazing">Image dehazing</a> |
| -- | <a href="Deep learning">Deep learning</a> |
| -- | <a href="Multiple feature extraction">Multiple feature extraction</a> |
| -- | <a href="Haze density map">Haze density map</a> |
| -- | <a href="Convolutional neural network">Convolutional neural network</a> |
| -- | <a href="Input transformation">Input transformation</a> |
| 773 0# - HOST ITEM ENTRY | |
| Host Biblionumber | 80269 |
| Host Itemnumber | 114212 |
| Place, publisher, and date of publication | New Delhi IETE |
| Title | IETE Journal of Research |
| International Standard Serial Number | 0377-2063 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Journal Article |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Location (home branch) | Sublocation or collection (holding branch) | Date acquired | Koha issues (times borrowed) | Piece designation (barcode) | Koha date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | SNDT Juhu | SNDT Juhu | 01/11/2025 | JP976.6 | 01/11/2025 | 01/11/2025 | Journal Article |