Imaging in low light is challenging due to low photon count and low SNR. Short-exposure
images suffer from noise, while long exposure can lead to blurry images and is often …

The current superresolution (SR) methods based on deep learning have shown remarkable
comparative advantages but remain unsatisfactory in recovering the high-frequency edge …

Due to the poor lighting condition and limited dynamic range of digital imaging devices, the
recorded images are often under-/over-exposed and with low contrast. Most of previous …

Performance is a critical challenge in mobile image processing. Given a reference imaging
pipeline, or even human-adjusted pairs of images, we seek to reproduce the enhancements …

We introduce a deep network architecture called DerainNet for removing rain streaks from
an image. Based on the deep convolutional neural network (CNN), we directly learn the …

Due to the spatially variant blur caused by camera shake and object motions under different
scene depths, deblurring images captured from dynamic scenes is challenging. Although …

Applying image processing algorithms independently to each frame of a video often leads to
undesired inconsistent results over time. Develo** temporally consistent video-based …

We present an approach to accelerating a wide variety of image processing operators. Our
approach uses a fully-convolutional network that is trained on input-output pairs that …

Demosaicking and denoising are the key first stages of the digital imaging pipeline but they
are also a severely ill-posed problem that infers three color values per pixel from a single …

Edge-preserving features (EPFs) obtained by the application of edge-preserving filters to
hyperspectral images (HSIs) have been found very effective in characterizing significant …