activity
20182021
most citedRecurrent Back-Projection Network for Video Super-Resolution

11 citations · 16 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CV20215 cited

Low Light Image Enhancement via Global and Local Context Modeling

Aditya Arora, Muhammad Haris, Syed Waqas Zamir +4

Images captured under low-light conditions manifest poor visibility, lack contrast and color vividness. Compared to conventional approaches, deep convolutional neural networks (CNN…

eess.IV2020

Image Super-Resolution using Explicit Perceptual Loss

Tomoki Yoshida, Kazutoshi Akita, Muhammad Haris +1

This paper proposes an explicit way to optimize the super-resolution network for generating visually pleasing images. The previous approaches use several loss functions which is ha…

cs.CV2020

Space-Time-Aware Multi-Resolution Video Enhancement

Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita

We consider the problem of space-time super-resolution (ST-SR): increasing spatial resolution of video frames and simultaneously interpolating frames to increase the frame rate. Mo…

cs.CV2019

Deep Back-Projection Networks for Single Image Super-resolution

Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita

Previous feed-forward architectures of recently proposed deep super-resolution networks learn the features of low-resolution inputs and the non-linear mapping from those to a high-…

cs.CV201911 cited

Recurrent Back-Projection Network for Video Super-Resolution

Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita

We proposed a novel architecture for the problem of video super-resolution. We integrate spatial and temporal contexts from continuous video frames using a recurrent encoder-decode…

cs.CV2018

Task-Driven Super Resolution: Object Detection in Low-resolution Images

Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita

We consider how image super resolution (SR) can contribute to an object detection task in low-resolution images. Intuitively, SR gives a positive impact on the object detection tas…