Coding principles are central to understanding the organization of brain circuitry. Sparse
coding offers several advantages, but a near-consensus has developed that it only has …

Recently, several models based on deep neural networks have achieved great success in
terms of both reconstruction accuracy and computational performance for single image …

In this paper, we address a rain removal problem from a single image, even in the presence
of heavy rain and rain streak accumulation. Our core ideas lie in our new rain image model …

In this paper we present a method for learning a discriminative classifier from unlabeled or
partially labeled data. Our approach is based on an objective function that trades-off mutual …

Deep learning techniques have been successfully applied in many areas of computer vision,
including low-level image restoration problems. For image super-resolution, several models …

Abstract We introduce Embed to Control (E2C), a method for model learning and control of
non-linear dynamical systems from raw pixel images. E2C consists of a deep generative …

We show that existing upsampling operators can be unified using the notion of the index
function. This notion is inspired by an observation in the decoding process of deep image …

The recent phenomenal interest in convolutional neural networks (CNNs) must have made it
inevitable for the super-resolution (SR) community to explore its potential. The response has …

Depth boundaries often lose sharpness when upsampling from low-resolution (LR) depth
maps especially at large upscaling factors. We present a new method to address the …

Sparse coding (SC) plays an important role in versatile computer vision applications such as
image super-resolution (SR). Most of the previous SC based SR methods partition the image …