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Found 12 papers, 4 papers with code
Image Generation with Self Pixel-wise Normalization
Region-adaptive normalization (RAN) methods have been widely used in the generative adversarial network (GAN)-based image-to-image translation technique.
XYDeblur: Divide and Conquer for Single Image Deblurring
Many convolutional neural networks (CNNs) for single image deblurring employ a U-Net structure to estimate latent sharp images.
Rethinking Coarse-to-Fine Approach in Single Image Deblurring
Coarse-to-fine strategies have been extensively used for the architecture design of single image deblurring networks.
Ranked #5 on
Deblurring
on RealBlur-J
(using extra training data)
Content-aware Directed Propagation Network with Pixel Adaptive Kernel Attention
In addition, we propose an improved information aggregation module with PAKA, called the hierarchical PAKA module (HPM).
W-Net: Two-stage U-Net with misaligned data for raw-to-RGB mapping
Recent research on learning a mapping between raw Bayer images and RGB images has progressed with the development of deep convolutional neural networks.
Simple yet Effective Way for Improving the Performance of GAN
In adversarial learning, discriminator often fails to guide the generator successfully since it distinguishes between real and generated images using silly or non-robust features.
Fast and Accurate 3D Hand Pose Estimation via Recurrent Neural Network for Capturing Hand Articulations
In this paper, we propose a hierarchically-structured convolutional recurrent neural network (HCRNN) with six branches that estimate the 3D position of the palm and five fingers independently.
AIM 2019 Challenge on Image Demoireing: Methods and Results
A new dataset, called LCDMoire was created for this challenge, and consists of 10, 200 synthetically generated image pairs (moire and clean ground truth).
cGANs with Conditional Convolution Layer
Conditional generative adversarial networks (cGANs) have been widely researched to generate class conditional images using a single generator.
PEPSI++: Fast and Lightweight Network for Image Inpainting
To address this problem, we propose a novel network architecture called PEPSI: parallel extended-decoder path for semantic inpainting network, which aims at reducing the hardware costs and improving the inpainting performance.
Unsupervised Deep Contrast Enhancement with Power Constraint for OLED Displays
In the proposed method, the power consumption is constrained by simply reducing the brightness a certain ratio, whereas the perceived visual quality is preserved as much as possible by enhancing the contrast of the image using a convolutional neural network (CNN).
Parallel Feature Pyramid Network for Object Detection
To overcome this limitation, we propose a CNN-based object detection architecture, referred to as a parallel feature pyramid (FP) network (PFPNet), where the FP is constructed by widening the network width instead of increasing the network depth.
