arXiv subjects

Yong Seok Heo

Publications and source records attributed to Yong Seok Heo.

2 recordsLinked to original sources

arXiv2022

Continuous Facial Motion Deblurring

We introduce a novel framework for continuous facial motion deblurring that restores the continuous sharp moment latent in a single motion-blurred face image via a moment control factor. Although a motion-blurred image is the accumulated signal of continuous sharp moments during the exposure time, most existing single image deblurring approaches aim to restore a fixed number of frames using multiple networks and training stages. To address this problem, we propose a continuous facial motion deblurring network based on GAN (CFMD-GAN), which is a novel framework for restoring the continuous moment latent in a single motion-blurred face image with a single network and a single training stage. To stabilize the network training, we train the generator to restore continuous moments in the order determined by our facial motion-based reordering process (FMR) utilizing domain-specific knowledge of the face. Moreover, we propose an auxiliary regressor that helps our generator produce more accurate images by estimating continuous sharp moments. Furthermore, we introduce a control-adaptive (ContAda) block that performs spatially deformable convolution and channel-wise attention as a function of the control factor. Extensive experiments on the 300VW datasets demonstrate that the proposed framework generates a various number of continuous output frames by varying the moment control factor. Compared with the recent single-to-single image deblurring networks trained with the same 300VW training set, the proposed method show the superior performance in restoring the central sharp frame in terms of perceptual metrics, including LPIPS, FID and Arcface identity distance. The proposed method outperforms the existing single-to-video deblurring method for both qualitative and quantitative comparisons.

cs.CV↗

arXiv2021

Digital image quantification of rice sheath blight: Optimized segmentation and automatic classification

Rapid and accurate phenotypic screening of rice germplasms is crucial in screening for sources of rice sheath blight resistance. However, visual and/or caliper-based estimations of coalescing, necrotic, ShB disease lesions are time-consuming, labor-intensive and exposed to human rater subjectivity. Here, we propose the use of RGB images and image processing techniques to quantify ShB disease progression in terms of lesion height and diseased area. To be specific, we developed a pixel color- and coordinate-based K-Means Clustering (PCC-KMC) algorithm utilizing Mahalanobis metric aimed at accurate segmentation of symptomatic and non-symptomatic regions within rice stem images. The performance of PCC-KMC was evaluated using Lin's concordance correlation coefficient by comparing its results to visual measurements of ShB lesion height and to lesion/diseased area measured using ImageJ. Low bias and high precision were observed for absolute lesion height (bias=0.93, precision=0.94) and absolute symptomatic area (bias=0.98, precision=0.97) studies. Moreover, we introduced a convolutional neural network (CNN) for the automatic annotation on clusters, termed PCC-KMC-CNN. Our CNN was trained based on 85%:15% of composition for training and testing dataset from total 168 ShB-infected stem sample images, recording 92% accuracy and 0.21 loss. PCC-KMC-CNN also showed high accuracy and precision for the absolute lesion height (bias=0.86, precision=0.90) and absolute diseased area (bias=0.99, precision=0.97) studies. These results demonstrate that the present methodology has great potential and promise to substitute the traditional visual-based ShB disease severity assessment.

q-bio.QM↗