Search Results for author:
Found 6 papers, 1 papers with code
Learning Hierarchical Dynamics with Spatial Adjacency for Image Enhancement
In various real-world image enhancement applications, the degradations are always non-uniform or non-homogeneous and diverse, which challenges most deep networks with fixed parameters during the inference phase.
Ranked #14 on
Image Dehazing
on SOTS Indoor
Progressive residual learning for single image dehazing
The recent physical model-free dehazing methods have achieved state-of-the-art performances.
Progressive Depth Learning for Single Image Dehazing
However, we note that the guidance of the depth information for transmission estimation could remedy the decreased visibility as distances increase.
Discriminative Feature Learning with Foreground Attention for Person Re-Identification
Specifically, a novel foreground attentive subnetwork is designed to drive the network's attention, in which a decoder network is used to reconstruct the binary mask by using a novel local regression loss function, and an encoder network is regularized by the decoder network to focus its attention on the foreground persons.
Single Image Super Resolution - When Model Adaptation Matters
The internal contents of the low resolution input image is neglected with deep modeling despite the earlier works showing the power of using such internal priors.
Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network
To tackle with the second problem, a lightweight CNN architecture which has carefully designed width, depth and skip connections was proposed.
