189 citations · 237 across the 9 of their papers we have counts for
12 papers
ReBotNet: Fast Real-time Video Enhancement
Jeya Maria Jose Valanarasu, Rahul Garg, Andeep Toor +5
Most video restoration networks are slow, have high computational load, and can't be used for real-time video enhancement. In this work, we design an efficient and fast framework t…
A View Independent Classification Framework for Yoga Postures
Mustafa Chasmai, Nirjhar Das, Aman Bhardwaj +1
Yoga is a globally acclaimed and widely recommended practice for a healthy living. Maintaining correct posture while performing a Yogasana is of utmost importance. In this work, we…
Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image
Shumian Xin, Neal Wadhwa, Tianfan Xue +5
We present a method that takes as input a single dual-pixel image, and simultaneously estimates the image's defocus map -- the amount of defocus blur at each pixel -- and recovers…
Zoom-to-Inpaint: Image Inpainting with High-Frequency Details
Soo Ye Kim, Kfir Aberman, Nori Kanazawa +6
Although deep learning has enabled a huge leap forward in image inpainting, current methods are often unable to synthesize realistic high-frequency details. In this paper, we propo…
How to Train Neural Networks for Flare Removal
Yicheng Wu, Qiurui He, Tianfan Xue +4
When a camera is pointed at a strong light source, the resulting photograph may contain lens flare artifacts. Flares appear in a wide variety of patterns (halos, streaks, color ble…
Learned Dual-View Reflection Removal
Simon Niklaus, Xuaner Cecilia Zhang, Jonathan T. Barron +4
Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconv…
Learning to Autofocus
Charles Herrmann, Richard Strong Bowen, Neal Wadhwa +4
Autofocus is an important task for digital cameras, yet current approaches often exhibit poor performance. We propose a learning-based approach to this problem, and provide a reali…
DuNet: Learning Depth Estimation from Dual-Cameras and Dual-Pixels
Yinda Zhang, Neal Wadhwa, Sergio Orts-Escolano +3
Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges. We present a novel…
Learning Single Camera Depth Estimation using Dual-Pixels
Rahul Garg, Neal Wadhwa, Sameer Ansari +1
Deep learning techniques have enabled rapid progress in monocular depth estimation, but their quality is limited by the ill-posed nature of the problem and the scarcity of high qua…
Wireless Software Synchronization of Multiple Distributed Cameras
Sameer Ansari, Neal Wadhwa, Rahul Garg +1
We present a method for precisely time-synchronizing the capture of image sequences from a collection of smartphone cameras connected over WiFi. Our method is entirely software-bas…
Synthetic Depth-of-Field with a Single-Camera Mobile Phone
Neal Wadhwa, Rahul Garg, David E. Jacobs +7
Shallow depth-of-field is commonly used by photographers to isolate a subject from a distracting background. However, standard cell phone cameras cannot produce such images optical…
Aperture Supervision for Monocular Depth Estimation
Pratul P. Srinivasan, Rahul Garg, Neal Wadhwa +2
We present a novel method to train machine learning algorithms to estimate scene depths from a single image, by using the information provided by a camera's aperture as supervision…