Publication:
Local neighbourhood image properties for exposure region determination method in nonuniform illumination images
Local neighbourhood image properties for exposure region determination method in nonuniform illumination images
| cris.virtual.department | Universiti Malaysia Perlis | |
| cris.virtualsource.department | e1d43257-6f17-4e8c-a73d-8108b2cc978c | |
| dc.contributor.author | Nor Hidayah Saad | |
| dc.contributor.author | Nor Ashidi Mat Isa | |
| dc.contributor.author | Abdullah Amer Mohammed Salih | |
| dc.date.accessioned | 2025-12-16T02:05:56Z | |
| dc.date.available | 2025-12-16T02:05:56Z | |
| dc.date.issued | 2020-04 | |
| dc.description.abstract | During image acquisition, nonuniform illumination regions are produced due to several factors, such as improper environment lighting and inappropriate capturing device setting. Applying contrast enhancement methods with the same enhancement concept to the whole image can over enhance or under enhance nonuniform illumination image. Thus, different and specific enhancement concepts should be applied to different regions in nonuniform illumination image. This concept requires identification of those different regions. Almost all existing methods that introduced the region determination process can only detect two different regions, namely, dark and bright, which inadequately represent the real exposure condition because the methods only consider intensity criteria to determine the regions. For this problem, a new method used for the accurate detection of nonuniform illumination regions is proposed. Different illumination levels affect not only the intensity but also the details in an image. Thus, three image attributes, namely, intensity, entropy and contrast, which are evaluated locally in detecting the regions, must be considered. For the detection to be on par with that in humans, the three attributes are combined with a rule-based method for the identification of illumination regions. Experimental results involving evaluation from research experts demonstrate that the proposed method qualitatively detects different illumination regions (i.e., over-exposed, well-exposed and under-exposed) in a nonuniform illumination image more accurate than the state-of-the-art methods. | |
| dc.identifier.doi | 10.1109/ACCESS.2020.2990730 | |
| dc.identifier.uri | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9079502 | |
| dc.identifier.uri | https://ieeexplore.ieee.org/ | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14170/15490 | |
| dc.language.iso | en | |
| dc.publisher | IEEE | |
| dc.relation.ispartof | IEEE Access | |
| dc.relation.issn | 2169-3536 | |
| dc.subject | Exposure region determination | |
| dc.subject | Image contrast | |
| dc.subject | Image entropy | |
| dc.subject | Image intensity | |
| dc.subject | Nonuniform illumination image | |
| dc.title | Local neighbourhood image properties for exposure region determination method in nonuniform illumination images | |
| dc.type | journal-article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 79997 | |
| oaire.citation.startPage | 79977 | |
| oaire.citation.volume | 8 | |
| oairecerif.author.affiliation | Universiti Malaysia Perlis | |
| oairecerif.author.affiliation | Universiti Sains Malaysia | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |