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Found 10 papers, 4 papers with code
MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems.
Learning Inclusion Matching for Animation Paint Bucket Colorization
In this work, we introduce a new learning-based inclusion matching pipeline, which directs the network to comprehend the inclusion relationships between segments rather than relying solely on direct visual correspondences.
Flare7K++: Mixing Synthetic and Real Datasets for Nighttime Flare Removal and Beyond
To address this issue, we additionally provide the annotations of light sources in Flare7K++ and propose a new end-to-end pipeline to preserve the light source while removing lens flares.
MIPI 2023 Challenge on Nighttime Flare Removal: Methods and Results
In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2023.
MIPI 2023 Challenge on RGB+ToF Depth Completion: Methods and Results
To evaluate the performance of different depth completion methods, we organized an RGB+sparse ToF depth completion competition.
MIPI 2023 Challenge on RGBW Remosaic: Methods and Results
Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms.
MIPI 2023 Challenge on RGBW Fusion: Methods and Results
Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms.
Nighttime Smartphone Reflective Flare Removal Using Optical Center Symmetry Prior
With the dataset, neural networks can be trained to remove the reflective flares effectively.
BeautyREC: Robust, Efficient, and Content-preserving Makeup Transfer
The key insights of this study are modeling component-specific correspondence for local makeup transfer, capturing long-range dependencies for global makeup transfer, and enabling efficient makeup transfer via a single-path structure.
Flare7K: A Phenomenological Nighttime Flare Removal Dataset
In this paper, we introduce, Flare7K, the first nighttime flare removal dataset, which is generated based on the observation and statistics of real-world nighttime lens flares.
Ranked #2 on
Flare Removal
on Flare7K
