EasySpec: Automatic Specular Reflection Detection and Suppression From Endoscopic Images

SpecReFlow: an algorithm for specular reflection restoration using flow-guided video completion

“…The order of the entire dataset was then randomized to ensure sample independence. The train/validation/test split was 80/10/10 based on common splits in other papers 4 , 19 …”

“…[11][12][13] In addition, conventional methods for SR restorations largely depend on empirical parameter setting, which can lead to slow runtimes and inaccurate feature appearance such as blurriness, structural inaccuracy, and poor color blending with the surrounding non-SR region. 6,[11][12][13][14][15][16][17] Many single-frame methods use deep learning approaches, such as convolutional neural networks (CNNs) 18,19 or generative adversarial networks, 4,5,[20][21][22] to directly inpaint the region for restoration. Although learned methods may lead to fast and high-quality restorations, the generated regions may not be truly representative of the ground-truth tissue, as they only inpaint the missing SR region using local spatial information obtained from a single perspective.…”

“…The train/validation/test split was 80∕10∕10 based on common splits in other papers. 4,19 3.3 Algorithm Design…”

“…Many single-frame methods use deep learning approaches, such as convolutional neural networks (CNNs) 18 , 19 or generative adversarial networks, 4 , 5 , 20 – 22 to directly inpaint the region for restoration. Although learned methods may lead to fast and high-quality restorations, the generated regions may not be truly representative of the ground-truth tissue, as they only inpaint the missing SR region using local spatial information obtained from a single perspective.…”

Development of endoscopic SLAM and the challenges

“…An encoder-decoder network pre-trained on non-surgical data with groundtruth was utilized in [12] to inpaint gastrointestinal images with light reflections. A weakly supervised approach employed a two-stage network to detect and suppress specular highlights on laparoscopy endometriosis images using U-Net architecture [13]. In [14], they fine-tuned a Spatial-Temporal Transformer Network with pseudo-groundtruth of specularity masks, achieving an unsupervised approach to remove specular highlight on gastric videos.…”