Learning a joint affinity graph for multiview subspace clustering
With the ability to exploit the internal structure of data, graph-based models have received a
lot of attention and have achieved great success in multiview subspace clustering for …
lot of attention and have achieved great success in multiview subspace clustering for …
Sharpness-aware low-dose CT denoising using conditional generative adversarial network
Low-dose computed tomography (LDCT) has offered tremendous benefits in radiation-
restricted applications, but the quantum noise as resulted by the insufficient number of …
restricted applications, but the quantum noise as resulted by the insufficient number of …
DeFusionNET: Defocus blur detection via recurrently fusing and refining discriminative multi-scale deep features
Albeit great success has been achieved in image defocus blur detection, there are still
several unsolved challenges, eg, interference of background clutter, scale sensitivity and …
several unsolved challenges, eg, interference of background clutter, scale sensitivity and …
Spatially-varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes
The detection of spatially-varying blur without having any information about the blur type is a
challenging task. In this paper, we propose a novel effective approach to address this blur …
challenging task. In this paper, we propose a novel effective approach to address this blur …
Defusionnet: Defocus blur detection via recurrently fusing and refining multi-scale deep features
Defocus blur detection aims to detect out-of-focus regions from an image. Although attracting
more and more attention due to its widespread applications, defocus blur detection still …
more and more attention due to its widespread applications, defocus blur detection still …
Defocus blur detection via multi-stream bottom-top-bottom fully convolutional network
Defocus blur detection (DBD) is the separation of infocus and out-of-focus regions in an
image. This process has been paid considerable attention because of its remarkable …
image. This process has been paid considerable attention because of its remarkable …
Defocus blur detection via multi-stream bottom-top-bottom network
Defocus blur detection (DBD) is aimed to estimate the probability of each pixel being in-
focus or out-of-focus. This process has been paid considerable attention due to its …
focus or out-of-focus. This process has been paid considerable attention due to its …
Enhancing diversity of defocus blur detectors via cross-ensemble network
Defocus blur detection (DBD) is a fundamental yet challenging topic, since the
homogeneous region is obscure and the transition from the focused area to the unfocused …
homogeneous region is obscure and the transition from the focused area to the unfocused …
Full-scene defocus blur detection with defbd+ via multi-level distillation learning
Existing defocus blur detection (DBD) methods generally perform well on a single type of
unfocused blur scene (eg, foreground focus), thereby suffering from the performance …
unfocused blur scene (eg, foreground focus), thereby suffering from the performance …
BRNet: Defocus Blur Detection Via a Bidirectional Channel Attention Residual Refining Network
Due to the remarkable potential applications, defocus blur detection, which aims to separate
blurry regions from an image, has attracted much attention. Although significant progress …
blurry regions from an image, has attracted much attention. Although significant progress …