Local Contrast Criterion for Verifiable Image Enhancement Network: Layered Difference Representation Loss
Deep neural networks (DNN) have made significant improvements in image processing, particularly in media forensic investigations. However, the resulting images or frames from DNN-based algorithms are typically not admissible as evidence because these algorithms do not precisely verify the internal p...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10786199/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850100823608000512 |
|---|---|
| author | Jin-Hwan Kim Hanul Kim Jae Sung Lim Nam In Park |
| author_facet | Jin-Hwan Kim Hanul Kim Jae Sung Lim Nam In Park |
| author_sort | Jin-Hwan Kim |
| collection | DOAJ |
| description | Deep neural networks (DNN) have made significant improvements in image processing, particularly in media forensic investigations. However, the resulting images or frames from DNN-based algorithms are typically not admissible as evidence because these algorithms do not precisely verify the internal processes from input to output. This study proposes an efficient local contrast enhancement criterion for a layered difference representation (LDR) loss, a verifiable image enhancement network. The LDR originally derives a transformation function based on neighboring pixel value differences. However, appending an additional constraint, such as image similarity to the ground truth, is challenging. To address this, we utilize DNNs and introduce a novel criterion, LDR loss, for image enhancement. The LDR loss aims to increase neighboring pixel differences, whereas the image loss ensures similarity with the ground truth, thus enhancing both global and local contrasts of the image. Experimental results demonstrate that the proposed algorithm outperforms conventional image enhancement algorithms. |
| format | Article |
| id | doaj-art-5bfde236024b48b19e45ba97c03efaba |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5bfde236024b48b19e45ba97c03efaba2025-08-20T02:40:11ZengIEEEIEEE Access2169-35362024-01-011219168219169410.1109/ACCESS.2024.351341910786199Local Contrast Criterion for Verifiable Image Enhancement Network: Layered Difference Representation LossJin-Hwan Kim0https://orcid.org/0009-0003-5930-7795Hanul Kim1https://orcid.org/0000-0001-7450-6600Jae Sung Lim2https://orcid.org/0009-0004-5886-3850Nam In Park3Digital Analysis Division, National Forensic Service, Wonju, South KoreaDept. of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, South KoreaDigital Analysis Division, National Forensic Service, Wonju, South KoreaDigital Analysis Division, National Forensic Service, Wonju, South KoreaDeep neural networks (DNN) have made significant improvements in image processing, particularly in media forensic investigations. However, the resulting images or frames from DNN-based algorithms are typically not admissible as evidence because these algorithms do not precisely verify the internal processes from input to output. This study proposes an efficient local contrast enhancement criterion for a layered difference representation (LDR) loss, a verifiable image enhancement network. The LDR originally derives a transformation function based on neighboring pixel value differences. However, appending an additional constraint, such as image similarity to the ground truth, is challenging. To address this, we utilize DNNs and introduce a novel criterion, LDR loss, for image enhancement. The LDR loss aims to increase neighboring pixel differences, whereas the image loss ensures similarity with the ground truth, thus enhancing both global and local contrasts of the image. Experimental results demonstrate that the proposed algorithm outperforms conventional image enhancement algorithms.https://ieeexplore.ieee.org/document/10786199/Contrast enhancementdeep neural networkimage enhancementtransformation function |
| spellingShingle | Jin-Hwan Kim Hanul Kim Jae Sung Lim Nam In Park Local Contrast Criterion for Verifiable Image Enhancement Network: Layered Difference Representation Loss IEEE Access Contrast enhancement deep neural network image enhancement transformation function |
| title | Local Contrast Criterion for Verifiable Image Enhancement Network: Layered Difference Representation Loss |
| title_full | Local Contrast Criterion for Verifiable Image Enhancement Network: Layered Difference Representation Loss |
| title_fullStr | Local Contrast Criterion for Verifiable Image Enhancement Network: Layered Difference Representation Loss |
| title_full_unstemmed | Local Contrast Criterion for Verifiable Image Enhancement Network: Layered Difference Representation Loss |
| title_short | Local Contrast Criterion for Verifiable Image Enhancement Network: Layered Difference Representation Loss |
| title_sort | local contrast criterion for verifiable image enhancement network layered difference representation loss |
| topic | Contrast enhancement deep neural network image enhancement transformation function |
| url | https://ieeexplore.ieee.org/document/10786199/ |
| work_keys_str_mv | AT jinhwankim localcontrastcriterionforverifiableimageenhancementnetworklayereddifferencerepresentationloss AT hanulkim localcontrastcriterionforverifiableimageenhancementnetworklayereddifferencerepresentationloss AT jaesunglim localcontrastcriterionforverifiableimageenhancementnetworklayereddifferencerepresentationloss AT naminpark localcontrastcriterionforverifiableimageenhancementnetworklayereddifferencerepresentationloss |