Banca de DEFESA: Muhammad Irshad

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
DISCENTE : Muhammad Irshad
DATA : 25/11/2022
HORA: 09:00
LOCAL: Videoconferência
TÍTULO:

Quality Assessment of Enhanced Underwater Images with Convolutional Neural Network

PALAVRAS-CHAVES:

Image Quality Assessment, Enhanced Images, Underwater Images, MSLBP, Convolutional Neural Networks

PÁGINAS: 102
RESUMO:

Image enhancement algorithms have the goal of improving the image quality and, therefore, the usefulness of an image for a given task. Although there are several image enhancement algorithms, there is no consensus on how to estimate the performance of these enhancement algorithms. Since the final consumers of the resulting enhanced visual content are human viewers, the performance of these algorithms should take into account the perceived visual quality of the resulting enhanced images. Unfortunately, although in the last decades a lot of progress has been made in the area of image quality assessment, designing metrics to estimate the quality of enhanced and restored images remains a challenge. This is particularly true for underwater image application, where images frequently need to be restored because of the severity of the degradations introduced by the underwater environment. Therefore, there is a great need for quality metrics that can estimate the quality of enhanced and restored images. In this thesis, our goal is to design metrics for this scenario. First, we have designed a quality metric based on texture operators and saliency. Second, we also designed a quality metric based on a deep learning architecture convolutional Neural Network (CNN). Experimental results on the underwater image database demonstrate that our approaches outperform the state-of-art methods compared. Third, we have developed a new dataset for Underwater image quality assessment. Additionally, we also present a psychophysical study based on crowd-sourcing interface, in which we analyze the perceptual quality of images enhanced with several types of enhancement algorithms. In this experiment, we have developed a database that can be used to train image quality metrics, and also can detect both increments and decrements in the perceived quality.

MEMBROS DA BANCA:
Externo à Instituição - WAMBERTO JOSÉ LIRA DE QUEIROZ - UFCG
Externo ao Programa - 2343453 - CRISTIANO JACQUES MIOSSO RODRIGUES MENDES
Interno - 3374036 - JOAO LUIZ AZEVEDO DE CARVALHO
Externo à Instituição - JOSE GABRIEL RODRIGUEZ CARNEIRO GOMES - UFRJ
Presidente - 1609346 - MYLENE CHRISTINE QUEIROZ DE FARIAS