Search Results for author:
Found 6 papers, 2 papers with code
Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time
The small CNN works on the down-sampled version of the input image to predict content-dependent weights to fuse the multiple basis 3D LUTs into an image-adaptive one, which is employed to transform the color and tone of source images efficiently.
Ranked #5 on
Image Enhancement
on MIT-Adobe 5k
(SSIM on proRGB metric)
Dark and Bright Channel Prior Embedded Network for Dynamic Scene Deblurring
In this work, we propose a Dark and Bright Channel Priors embedded Network (DBCPeNet) to plug the channel priors into a neural network for effective dynamic scene deblurring.
Ranked #32 on
Image Deblurring
on GoPro
(using extra training data)
CameraNet: A Two-Stage Framework for Effective Camera ISP Learning
Traditional image signal processing (ISP) pipeline consists of a set of individual image processing components onboard a camera to reconstruct a high-quality sRGB image from the sensor raw data.
Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model
Most of the existing learning-based single image superresolution (SISR) methods are trained and evaluated on simulated datasets, where the low-resolution (LR) images are generated by applying a simple and uniform degradation (i. e., bicubic downsampling) to their high-resolution (HR) counterparts.
Extreme Channel Prior Embedded Network for Dynamic Scene Deblurring
In this work, we propose an Extreme Channel Prior embedded Network (ECPeNet) to plug the extreme channel priors (i. e., priors on dark and bright channels) into a network architecture for effective dynamic scene deblurring.
Learning a Single Tucker Decomposition Network for Lossy Image Compression with Multiple Bits-Per-Pixel Rates
By changing the rank of the core tensor and its quantization, we can easily adjust the bpp rate of latent image representation within a single CNN.
