Guided Unsupervised Desmoking of Laparoscopic Images Using Cycle-Desmoke

Vishal, V.; Sharma, Neeraj; Singh, Munendra

doi:10.1007/978-3-030-32695-1_3

Cited by 6 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers employed different NR-IQA methodologies to evaluate the quality of the recovered images in their studies [10][11][12][13][14][15][16]. Table 1 presents various assessment techniques that have been utilized in the process of image evaluation in terms of laparoscopic images analysis, as well as the comparison between these IQAs.…”

Section: Nr-iqamentioning

confidence: 99%

A Brief Review of Image Quality Assessment Techniques for Laparoscopic Image Restoration

2022

IJFM

View full text Add to dashboard Cite

Laparoscopic images usually suffer image degradation due to smoking, insufficient illumination, specularity, and limited fields of view. Computer-assisted restoration algorithms have been developed to tackle this problem, whose performance needs to be assessed through a mechanism of image quality evaluation. In this study, we will briefly exam several image quality assessment technologies that have been utilized in the process of laparoscopic image restoration.

show abstract

Section: Nr-iqamentioning

confidence: 99%

A Brief Review of Image Quality Assessment Techniques for Laparoscopic Image Restoration

2022

IJFM

View full text Add to dashboard Cite

show abstract

“…In [17], a convolutional neural network (CNN) takes the smoke image and its pyramidal decomposition as inputs, and then, it directly outputs a smokefree image without relying on any physical model and with no estimation of intermediate parameters. Also, in [18], it is implemented an end-to-end network called Cycle-Desmoke. Such a network uses a generator architecture with two-loss functions: a guided-unsharp upsample loss function and an adversarial and cycle-consistency loss function.…”

mentioning

confidence: 99%