Reflection Removal
29 papers with code • 5 benchmarks • 3 datasets
Most implemented papers
Polarized Reflection Removal with Perfect Alignment in the Wild
We present a novel formulation to removing reflection from polarized images in the wild.
Learning to See Through Obstructions
We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequence of images captured by a moving camera.
Deep-Masking Generative Network: A Unified Framework for Background Restoration from Superimposed Images
In this work we present the Deep-Masking Generative Network (DMGN), which is a unified framework for background restoration from the superimposed images and is able to cope with different types of noise.
Two-Stage Single Image Reflection Removal with Reflection-Aware Guidance
To be specific, the reflection layer is firstly estimated due to that it generally is much simpler and is relatively easier to estimate.
Location-aware Single Image Reflection Removal
It is beneficial to strong reflection detection and substantially improves the quality of reflection removal results.
V-DESIRR: Very Fast Deep Embedded Single Image Reflection Removal
Our method processes the corrupted image in two stages, a Low Scale Sub-network (LSSNet) to process the lowest scale and a Progressive Inference (PI) stage to process all the higher scales.
Robust Reflection Removal with Reflection-free Flash-only Cues
The flash-only image is equivalent to an image taken in a dark environment with only a flash on.
Single Image Reflection Removal With Absorption Effect
In this paper, we consider the absorption effect for the problem of single image reflection removal.
Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning
Specifically, we show that jointly learning to predict the two DP views from a single blurry input image improves the network's ability to learn to deblur the image.
Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation
Single image reflection separation (SIRS), as a representative blind source separation task, aims to recover two layers, $\textit{i. e.}$, transmission and reflection, from one mixed observation, which is challenging due to the highly ill-posed nature.