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Found 69 papers, 40 papers with code
Is Underwater Image Enhancement All Object Detectors Need?
Coupled with 7 object detection models retrained using raw underwater images, we employ these 133 models to comprehensively analyze the effect of underwater image enhancement on underwater object detection.
LAMP: Learn A Motion Pattern for Few-Shot-Based Video Generation
Specifically, we design a first-frame-conditioned pipeline that uses an off-the-shelf text-to-image model for content generation so that our tuned video diffusion model mainly focuses on motion learning.
ProPainter: Improving Propagation and Transformer for Video Inpainting
We also propose a mask-guided sparse video Transformer, which achieves high efficiency by discarding unnecessary and redundant tokens.
Ranked #1 on
Video Inpainting
on DAVIS
Empowering Low-Light Image Enhancer through Customized Learnable Priors
In this work, we propose a paradigm for low-light image enhancement that explores the potential of customized learnable priors to improve the transparency of the deep unfolding paradigm.
Improving Lens Flare Removal with General Purpose Pipeline and Multiple Light Sources Recovery
In this paper, we propose a solution to improve the performance of lens flare removal by revisiting the ISP and remodeling the principle of automatic exposure in the synthesis pipeline and design a more reliable light sources recovery strategy.
Learned Image Reasoning Prior Penetrates Deep Unfolding Network for Panchromatic and Multi-Spectral Image Fusion
Such designs penetrate the image reasoning prior into deep unfolding networks while improving its interpretability and representation capability.
AMSP-UOD: When Vortex Convolution and Stochastic Perturbation Meet Underwater Object Detection
In this paper, we present a novel Amplitude-Modulated Stochastic Perturbation and Vortex Convolutional Network, AMSP-UOD, designed for underwater object detection.
Synergistic Multiscale Detail Refinement via Intrinsic Supervision for Underwater Image Enhancement
ASISF improves the multiscale detail refinement while reducing interference from irrelevant scene information from the low-degradation stage.
Lighting Every Darkness in Two Pairs: A Calibration-Free Pipeline for RAW Denoising
Calibration-based methods have dominated RAW image denoising under extremely low-light environments.
Ranked #1 on
Image Denoising
on SID SonyA7S2 x300
Decomposition Ascribed Synergistic Learning for Unified Image Restoration
Learning to restore multiple image degradations within a single model is quite beneficial for real-world applications.
Learnable Differencing Center for Nighttime Depth Perception
Depth completion is the task of recovering dense depth maps from sparse ones, usually with the help of color images.
Adaptive Window Pruning for Efficient Local Motion Deblurring
Existing image deblurring methods predominantly focus on global deblurring, inadvertently affecting the sharpness of backgrounds in locally blurred images and wasting unnecessary computation on sharp pixels, especially for high-resolution images.
PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators
Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability.
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.
Unlocking Low-Light-Rainy Image Restoration by Pairwise Degradation Feature Vector Guidance
P-Net can learn degradation feature vectors on the dark and light areas separately, using contrastive learning to guide the image restoration process.
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 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.
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.
Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement
To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model.
Ranked #3 on
Low-Light Image Enhancement
on LOLv2
Generating Aligned Pseudo-Supervision from Non-Aligned Data for Image Restoration in Under-Display Camera
Due to the difficulty in collecting large-scale and perfectly aligned paired training data for Under-Display Camera (UDC) image restoration, previous methods resort to monitor-based image systems or simulation-based methods, sacrificing the realness of the data and introducing domain gaps.
Iterative Prompt Learning for Unsupervised Backlit Image Enhancement
To solve this issue, we devise a prompt learning framework that first learns an initial prompt pair by constraining the text-image similarity between the prompt (negative/positive sample) and the corresponding image (backlit image/well-lit image) in the CLIP latent space.
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration
We investigate the efficacy of our belief from three perspectives: 1) from task-customized MAE to native MAE, 2) from image task to video task, and 3) from transformer structure to convolution neural network structure.
Random Weights Networks Work as Loss Prior Constraint for Image Restoration
In this paper, orthogonal to the existing data and model studies, we instead resort our efforts to investigate the potential of loss function in a new perspective and present our belief ``Random Weights Networks can Be Acted as Loss Prior Constraint for Image Restoration''.
Nighttime Smartphone Reflective Flare Removal Using Optical Center Symmetry Prior
With the dataset, neural networks can be trained to remove the reflective flares effectively.
Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement
Our approach is motivated by a few unique characteristics in the Fourier domain: 1) most luminance information concentrates on amplitudes while noise is closely related to phases, and 2) a high-resolution image and its low-resolution version share similar amplitude patterns. Through embedding Fourier into our network, the amplitude and phase of a low-light image are separately processed to avoid amplifying noise when enhancing luminance.
Low-Light Image Enhancement
Vocal Bursts Intensity Prediction
Troubleshooting Ethnic Quality Bias with Curriculum Domain Adaptation for Face Image Quality Assessment
Furthermore, we design an easy-to-hard training scheduler based on the inter-domain uncertainty and intra-domain quality margin as well as the ranking-based domain adversarial network to enhance the effectiveness of transfer learning and further reduce the source risk in domain adaptation.
DNF: Decouple and Feedback Network for Seeing in the Dark
The exclusive properties of RAW data have shown great potential for low-light image enhancement.
Improving Lens Flare Removal with General-Purpose Pipeline and Multiple Light Sources Recovery
In this paper, we propose a solution to improve the performance of lens flare removal by revisiting the ISP and remodeling the principle of automatic exposure in the synthesis pipeline and design a more reliable light sources recovery strategy.
Exploring Temporal Frequency Spectrum in Deep Video Deblurring
Video deblurring aims to restore the latent video frames from their blurred counterparts.
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.
Panchromatic and Multispectral Image Fusion via Alternating Reverse Filtering Network
Panchromatic (PAN) and multi-spectral (MS) image fusion, named Pan-sharpening, refers to super-resolve the low-resolution (LR) multi-spectral (MS) images in the spatial domain to generate the expected high-resolution (HR) MS images, conditioning on the corresponding high-resolution PAN images.
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
Deep Fourier Up-Sampling
Existing convolutional neural networks widely adopt spatial down-/up-sampling for multi-scale modeling.
CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection
Focusing on the issue of how to effectively capture and utilize cross-modality information in RGB-D salient object detection (SOD) task, we present a convolutional neural network (CNN) model, named CIR-Net, based on the novel cross-modality interaction and refinement.
MIPI 2022 Challenge on RGBW Sensor Fusion: Dataset and Report
A detailed description of all models developed in this challenge is provided in this paper.
MIPI 2022 Challenge on RGBW Sensor Re-mosaic: Dataset and Report
A detailed description of all models developed in this challenge is provided in this paper.
MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results
In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022.
MIPI 2022 Challenge on RGB+ToF Depth Completion: Dataset and Report
A detailed description of all models developed in this challenge is provided in this paper.
MIPI 2022 Challenge on Quad-Bayer Re-mosaic: Dataset and Report
A detailed description of all models developed in this challenge is provided in this paper.
Underwater Ranker: Learn Which Is Better and How to Be Better
To achieve that, we also contribute a dataset, URankerSet, containing sufficient results enhanced by different UIE algorithms and the corresponding perceptual rankings, to train our URanker.
CuDi: Curve Distillation for Efficient and Controllable Exposure Adjustment
Our method inherits the zero-reference learning and curve-based framework from an effective low-light image enhancement method, Zero-DCE, with further speed up in its inference speed, reduction in its model size, and extension to controllable exposure adjustment.
Towards Robust Blind Face Restoration with Codebook Lookup Transformer
In this paper, we demonstrate that a learned discrete codebook prior in a small proxy space largely reduces the uncertainty and ambiguity of restoration mapping by casting blind face restoration as a code prediction task, while providing rich visual atoms for generating high-quality faces.
Ranked #1 on
Blind Face Restoration
on CelebA-Test
Global-and-Local Collaborative Learning for Co-Salient Object Detection
In this paper, we propose a global-and-local collaborative learning architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture comprehensive inter-image corresponding relationship among different images from the global and local perspectives.
LEDNet: Joint Low-light Enhancement and Deblurring in the Dark
With the pipeline, we present the first large-scale dataset for joint low-light enhancement and deblurring.
Image Dehazing Transformer With Transmission-Aware 3D Position Embedding
Though Transformer has occupied various computer vision tasks, directly leveraging Transformer for image dehazing is challenging: 1) it tends to result in ambiguous and coarse details that are undesired for image reconstruction; 2) previous position embedding of Transformer is provided in logic or spatial position order that neglects the variational haze densities, which results in the sub-optimal dehazing performance.
Estimating Parameters of the Tree Root in Heterogeneous Soil Environments via Mask-Guided Multi-Polarimetric Integration Neural Network
However, the task of estimating root-related parameters is challenging as the root reflection is a complex function of multiple root parameters and root orientations.
Investigating Attention Mechanism in 3D Point Cloud Object Detection
Object detection in three-dimensional (3D) space attracts much interest from academia and industry since it is an essential task in AI-driven applications such as robotics, autonomous driving, and augmented reality.
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding
As a result, our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding and the advantages of both physical model-based and learning-based methods.
Ranked #2 on
Underwater Image Restoration
on LSUI
(using extra training data)
Low-Light Image and Video Enhancement Using Deep Learning: A Survey
Low-light image enhancement (LLIE) aims at improving the perception or interpretability of an image captured in an environment with poor illumination.
Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection Network
Recent development of Under-Display Camera (UDC) systems provides a true bezel-less and notch-free viewing experience on smartphones (and TV, laptops, tablets), while allowing images to be captured from the selfie camera embedded underneath.
Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation
This paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network.
The Orientation Estimation of Elongated Underground Objects via Multi-Polarization Aggregation and Selection Neural Network
The horizontal orientation angle and vertical inclination angle of an elongated subsurface object are key parameters for object identification and imaging in ground penetrating radar (GPR) applications.
Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images
Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains an open and challenging problem.
CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection
In the first stage, we propose a group-attentional semantic aggregation module that models inter-image relationships to generate the group-wise semantic representations.
Flexible Piecewise Curves Estimation for Photo Enhancement
This paper presents a new method, called FlexiCurve, for photo enhancement.
A Parallel Down-Up Fusion Network for Salient Object Detection in Optical Remote Sensing Images
In this paper, we propose a novel Parallel Down-up Fusion network (PDF-Net) for SOD in optical RSIs, which takes full advantage of the in-path low- and high-level features and cross-path multi-resolution features to distinguish diversely scaled salient objects and suppress the cluttered backgrounds.
Image Colorization: A Survey and Dataset
Image colorization is the process of estimating RGB colors for grayscale images or video frames to improve their aesthetic and perceptual quality.
NuI-Go: Recursive Non-Local Encoder-Decoder Network for Retinal Image Non-Uniform Illumination Removal
We further demonstrate the advantages of the proposed method for improving the accuracy of retinal vessel segmentation.
RGB-D Salient Object Detection with Cross-Modality Modulation and Selection
Second, we propose an adaptive feature selection (AFS) module to select saliency-related features and suppress the inferior ones.
Ranked #8 on
RGB-D Salient Object Detection
on NJU2K
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network.
Ranked #1 on
Color Constancy
on INTEL-TUT2
UW-NET: AN INCEPTION-ATTENTION NETWORK FOR UNDERWATER IMAGE CLASSIFICATION
By substituting the inception module with the I-A module, the Inception-ResnetV2 network achieves a 10. 7% top1 error rate and a 0% top5 error rate on the subset of ILSVRC-2012, which further illustrates the function of the background attention in the image classifications.
Diving Deeper into Underwater Image Enhancement: A Survey
In this paper, our main aim is two-fold, 1): to provide a comprehensive and in-depth survey of the deep learning-based underwater image enhancement, which covers various perspectives ranging from algorithms to open issues, and 2): to conduct a qualitative and quantitative comparison of the deep algorithms on diverse datasets to serve as a benchmark, which has been barely explored before.
Nested Network with Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images
Arising from the various object types and scales, diverse imaging orientations, and cluttered backgrounds in optical remote sensing image (RSI), it is difficult to directly extend the success of salient object detection for nature scene image to the optical RSI.
An Underwater Image Enhancement Benchmark Dataset and Beyond
In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images.
Ranked #5 on
Underwater Image Restoration
on LSUI
(using extra training data)
Deep Underwater Image Enhancement
In an underwater scene, wavelength-dependent light absorption and scattering degrade the visibility of images, causing low contrast and distorted color casts.
A Cascaded Convolutional Neural Network for Single Image Dehazing
Different from previous learning-based methods, we propose a flexible cascaded CNN for single hazy image restoration, which considers the medium transmission and global atmospheric light jointly by two task-driven subnetworks.
DR-Net: Transmission Steered Single Image Dehazing Network with Weakly Supervised Refinement
Despite the recent progress in image dehazing, several problems remain largely unsolved such as robustness for varying scenes, the visual quality of reconstructed images, and effectiveness and flexibility for applications.
Emerging from Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer
Underwater vision suffers from severe effects due to selective attenuation and scattering when light propagates through water.
