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Found 14 papers, 4 papers with code
Du²Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels
Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges.
ReBotNet: Fast Real-time Video Enhancement
The first branch learns spatio-temporal features by tokenizing the input frames along the spatial and temporal dimensions using a ConvNext-based encoder and processing these abstract tokens using a bottleneck mixer.
A View Independent Classification Framework for Yoga Postures
We argue that for most of the applications, validation accuracies on unseen subjects and unseen camera angles would be most important.
Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image
We use data captured with a consumer smartphone camera to demonstrate that, after a one-time calibration step, our approach improves upon prior works for both defocus map estimation and blur removal, despite being entirely unsupervised.
Zoom-to-Inpaint: Image Inpainting with High-Frequency Details
Although deep learning has enabled a huge leap forward in image inpainting, current methods are often unable to synthesize realistic high-frequency details.
How to Train Neural Networks for Flare Removal
When a camera is pointed at a strong light source, the resulting photograph may contain lens flare artifacts.
Learned Dual-View Reflection Removal
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 inconvenient for users.
Learning to Autofocus
Autofocus is an important task for digital cameras, yet current approaches often exhibit poor performance.
Du$^2$Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels
Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges.
Learning Single Camera Depth Estimation using Dual-Pixels
Using our approach, existing monocular depth estimation techniques can be effectively applied to dual-pixel data, and much smaller models can be constructed that still infer high quality depth.
Wireless Software Synchronization of Multiple Distributed Cameras
We present a method for precisely time-synchronizing the capture of image sequences from a collection of smartphone cameras connected over WiFi.
Synthetic Depth-of-Field with a Single-Camera Mobile Phone
Shallow depth-of-field is commonly used by photographers to isolate a subject from a distracting background.
Aperture Supervision for Monocular Depth Estimation
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.
