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Found 15 papers, 8 papers with code
Learning Real-World Image De-Weathering with Imperfect Supervision
Real-world image de-weathering aims at removing various undesirable weather-related artifacts.
Self-Supervised High Dynamic Range Imaging with Multi-Exposure Images in Dynamic Scenes
The color component is estimated from aligned multi-exposure images, while the structure one is generated through a structure-focused network that is supervised by the color component and an input reference (\eg, medium-exposure) image.
Beyond Image Borders: Learning Feature Extrapolation for Unbounded Image Composition
In this paper, we circumvent this issue by presenting a joint framework for both unbounded recommendation of camera view and image composition (i. e., UNIC).
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions.
Self-supervised Learning to Bring Dual Reversed Rolling Shutter Images Alive
In this paper, we propose a Self-supervised learning framework for Dual reversed RS distortions Correction (SelfDRSC), where a DRSC network can be learned to generate a high framerate GS video only based on dual RS images with reversed distortions.
Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising
And we extend the blind-spot network to a blind-neighborhood network (BNN) for providing supervision on flat areas.
CoSign: Exploring Co-occurrence Signals in Skeleton-based Continuous Sign Language Recognition
The co-occurrence signals (e. g., hand shape, facial expression, and lip pattern) play a critical role in Continuous Sign Language Recognition (CSLR).
Physics-Guided ISO-Dependent Sensor Noise Modeling for Extreme Low-Light Photography
Although deep neural networks have achieved astonishing performance in many vision tasks, existing learning-based methods are far inferior to the physical model-based solutions in extreme low-light sensor noise modeling.
Ranked #1 on
Image Denoising
on ELD SonyA7S2 x200
Deep Radial Embedding for Visual Sequence Learning
The blank class of CTC plays a crucial role in the alignment process and is often considered responsible for the peaky behavior of CTC.
Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report
The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing.
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks.
Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report
In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based bokeh effect rendering approach that can run on modern smartphone GPUs using TensorFlow Lite.
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP).
AIM 2022 Challenge on Instagram Filter Removal: Methods and Results
This paper introduces the methods and the results of AIM 2022 challenge on Instagram Filter Removal.
Semantic Segmentation on VSPW Dataset through Aggregation of Transformer Models
Semantic segmentation is an important task in computer vision, from which some important usage scenarios are derived, such as autonomous driving, scene parsing, etc.
