Publication
PROCESSING OF MICROSCOPY IMAGES IN THE COMPRESSED DOMAIN
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | |
| dc.contributor.advisor | Thomaz, Lucas Arrabal | |
| dc.contributor.advisor | Távora, Luís Miguel de Oliveira Pegado de Noronha e | |
| dc.contributor.advisor | Grilo, Carlos Fernando de Almeida | |
| dc.contributor.advisor | Miragaia, Rolando Lúcio Germano | |
| dc.contributor.author | Vasconcellos, Nicolas David Freire | |
| dc.date.accessioned | 2025-09-02T11:17:11Z | |
| dc.date.available | 2025-09-02T11:17:11Z | |
| dc.date.issued | 2025-03-19 | |
| dc.description.abstract | This work presents a novel approach to the detection of mitochondria in microscopy images within the compressed domain, eliminating the need for full image decompression while maintaining high detection accuracy. The proposed architecture, Processing of Microscopy Images in the Compressed Domain (ProMIC), integrates a Domain Translator architecture, enabling object detection networks to operate directly on images’ latent representations. This approach significantly reduces computational costs while preserving essential visual information for detection tasks. The study also included a preliminary analysis of JPEG-AI compression efficiency against classical codecs (HEVC/H.265 and VVC/H.266) across multiple datasets. The proposed method builds upon a Base model inspired by a previous compresseddomain object detection approach. Key innovations in this work include: the use of a Lightweight Residual Block that enhances feature extraction from latent representations, improving detection robustness across different compression rates; a Pixel Domain Fine-Tuning process that enhances the Domain Adaptation of the model; and a Guided Domain Translator Training strategy that refines the Domain Translator to extract meaningful information directly from latent codes, minimising the gap between compressed and pixel-domain object detection models. Extensive experiments were conducted to compare the performance of both the Base model and the proposed architecture against a reference object detection model that operates on reconstructed images. Results on the Lucchi++ dataset show that ProMIC significantly outperforms the Base model. Notably, the proposed architecture exceeds the reference model in mean average precision, achieving a 2.31 percentage point improvement at the highest bitrate. This highlights the potential of compressed-domain image processing to capture meaningful features that may be lost in fully reconstructed images. Additionally, applying the ProMIC method reduced the complexity of the object detection system by 42.34% compared to the reference model. These findings validate the feasibility of direct object detection in latent codes, offering a computationally efficient alternative to traditional pixel-domain approaches. The proposed framework has promising applications in real-time processing, embedded systems, and large-scale AI-driven image analysis, paving the way for more efficient and scalable vision models that operate directly in the compressed domain. | por |
| dc.identifier.tid | 203996577 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/13949 | |
| dc.language.iso | eng | |
| dc.relation | Compression of Multimodal Biomedical Images using Neural Networks | |
| dc.relation | Instituto de Telecomunicações | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Engenharia eletrónica | |
| dc.subject | Imagem | |
| dc.subject | Computação | |
| dc.subject | Imagem por computação | |
| dc.subject | Imagem de Microscopia no domínio comprimido | |
| dc.title | PROCESSING OF MICROSCOPY IMAGES IN THE COMPRESSED DOMAIN | |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Compression of Multimodal Biomedical Images using Neural Networks | |
| oaire.awardTitle | Instituto de Telecomunicações | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/3599-PPCDT/2022.09914.PTDC/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT | |
| oaire.fundingStream | 3599-PPCDT | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| relation.isProjectOfPublication | 5fe22f63-2c55-4683-bae7-89d5dba06107 | |
| relation.isProjectOfPublication | 0836c6a6-afd0-499e-8a16-612dd27ec1dc | |
| relation.isProjectOfPublication.latestForDiscovery | 5fe22f63-2c55-4683-bae7-89d5dba06107 | |
| thesis.degree.name | Master in Electrical Engineering |