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Found 48 papers, 17 papers with code
Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement
Getting rid of the fundamental limitations in fitting to the paired training data, recent unsupervised low-light enhancement methods excel in adjusting illumination and contrast of images.
Feature-Aligned Video Raindrop Removal with Temporal Constraints
Our single image module employs a raindrop removal network to generate initial raindrop removal results, and create a mask representing the differences between the input and initial output.
UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learning
From a different yet new perspective, this paper explores contrastive learning with an adversarial training effort to leverage unpaired real-world hazy and clean images, thus bridging the gap between synthetic and real-world haze is avoided.
Semi-DRDNet Semi-supervised Detail-recovery Image Deraining Network via Unpaired Contrastive Learning
Such degradation is further exacerbated when applying the models trained on synthetic data to real-world rainy images.
Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond
Finally, we examine various types of adversarial attacks that are specific to deraining problems and their effects on both human and machine vision tasks, including 1) rain region attacks, adding perturbations only in the rain regions to make the perturbations in the attacked rain images less visible; 2) object-sensitive attacks, adding perturbations only in regions near the given objects.
Neural Data-Dependent Transform for Learned Image Compression
Learned image compression has achieved great success due to its excellent modeling capacity, but seldom further considers the Rate-Distortion Optimization (RDO) of each input image.
MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning
Prior works either simply align the global features of an image with its associated class semantic vector or utilize unidirectional attention to learn the limited latent semantic representations, which could not effectively discover the intrinsic semantic knowledge e. g., attribute semantics) between visual and attribute features.
The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement
In particular, we create a large-scale database for QUality assessment Of The Enhanced LOw-Light Image (QUOTE-LOL), which serves as the foundation in studying and developing objective quality assessment measures.
Enhancing Low-Light Images in Real World via Cross-Image Disentanglement
Images captured in the low-light condition suffer from low visibility and various imaging artifacts, e. g., real noise.
URetinex-Net: Retinex-Based Deep Unfolding Network for Low-Light Image Enhancement
Retinex model-based methods have shown to be effective in layer-wise manipulation with well-designed priors for low-light image enhancement.
Towards Low Light Enhancement with RAW Images
In this paper, we make the first benchmark effort to elaborate on the superiority of using RAW images in the low light enhancement and develop a novel alternative route to utilize RAW images in a more flexible and practical way.
Semantically Contrastive Learning for Low-light Image Enhancement
Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images.
Video Coding for Machine: Compact Visual Representation Compression for Intelligent Collaborative Analytics
By keeping in mind the transferability among different machine vision tasks (e. g. high-level semantic and mid-level geometry-related), we aim to support multiple tasks jointly at low bit rates.
Low-Light Image Enhancement with Normalizing Flow
To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the mapping relationship between them is one-to-many.
Ranked #2 on
Low-Light Image Enhancement
on LOL
Self-Aligned Video Deraining With Transmission-Depth Consistency
In this paper, we address the problems of rain streaks and rain accumulation removal in video, by developing a self-aligned network with transmission-depth consistency.
Revisit Visual Representation in Analytics Taxonomy: A Compression Perspective
Visual analytics have played an increasingly critical role in the Internet of Things, where massive visual signals have to be compressed and fed into machines.
HLA-Face: Joint High-Low Adaptation for Low Light Face Detection
To reduce the burden of building new datasets for low light conditions, we make full use of existing normal light data and explore how to adapt face detectors from normal light to low light.
Camera Invariant Feature Learning for Generalized Face Anti-spoofing
The first branch aims to learn the camera invariant spoofing features via feature level decomposition in the high frequency domain.
Towards Unsupervised Deep Image Enhancement with Generative Adversarial Network
In this paper, we present an unsupervised image enhancement generative adversarial network (UEGAN), which learns the corresponding image-to-image mapping from a set of images with desired characteristics in an unsupervised manner, rather than learning on a large number of paired images.
Unpaired Image Enhancement with Quality-Attention Generative Adversarial Network
The key novelty of the proposed QAGAN lies in the injected QAM for the generator such that it learns domain-relevant quality attention directly from the two domains.
Self-Learning Video Rain Streak Removal: When Cyclic Consistency Meets Temporal Correspondence
With this in mind, we construct a two-stage Self-Learned Deraining Network (SLDNet) to remove rain streaks based on both temporal correlation and consistency.
From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement
A deep recursive band network (DRBN) is proposed to recover a linear band representation of an enhanced normal-light image with paired low/normal-light images, and then obtain an improved one by recomposing the given bands via another learnable linear transformation based on a perceptual quality-driven adversarial learning with unpaired data.
Towards Analysis-friendly Face Representation with Scalable Feature and Texture Compression
In particular, we study the feature and texture compression in a scalable coding framework, where the base layer serves as the deep learning feature and enhancement layer targets to perfectly reconstruct the texture.
End-to-End Facial Deep Learning Feature Compression with Teacher-Student Enhancement
In this paper, we propose a novel end-to-end feature compression scheme by leveraging the representation and learning capability of deep neural networks, towards intelligent front-end equipped analysis with promising accuracy and efficiency.
Learning End-to-End Lossy Image Compression: A Benchmark
In this paper, we first conduct a comprehensive literature survey of learned image compression methods.
Combining Progressive Rethinking and Collaborative Learning: A Deep Framework for In-Loop Filtering
For (2), we extract both intra-frame and inter-frame side information for better context modeling.
Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics
Meanwhile, we systematically review state-of-the-art techniques in video compression and feature compression from the unique perspective of MPEG standardization, which provides the academic and industrial evidence to realize the collaborative compression of video and feature streams in a broad range of AI applications.
Towards Coding for Human and Machine Vision: A Scalable Image Coding Approach
In this paper, we come up with a novel image coding framework by leveraging both the compressive and the generative models, to support machine vision and human perception tasks jointly.
An Emerging Coding Paradigm VCM: A Scalable Coding Approach Beyond Feature and Signal
To this end, we make endeavors in leveraging the strength of predictive and generative models to support advanced compression techniques for both machine and human vision tasks simultaneously, in which visual features serve as a bridge to connect signal-level and task-level compact representations in a scalable manner.
Single Image Deraining: From Model-Based to Data-Driven and Beyond
The goal of single-image deraining is to restore the rain-free background scenes of an image degraded by rain streaks and rain accumulation.
A Comprehensive Benchmark for Single Image Compression Artifacts Reduction
We present a comprehensive study and evaluation of existing single image compression artifacts removal algorithms, using a new 4K resolution benchmark including diversified foreground objects and background scenes with rich structures, called Large-scale Ideal Ultra high definition 4K (LIU4K) benchmark.
Unsupervised Image Noise Modeling with Self-Consistent GAN
They jointly facilitate unsupervised learning of a noise model for various noise types.
Frame-Consistent Recurrent Video Deraining With Dual-Level Flow
The proposed framework is built upon a two-stage recurrent network with dual-level flow regularizations to perform the inverse recovery process of the rain synthesis model for video deraining.
Deep Reference Generation with Multi-Domain Hierarchical Constraints for Inter Prediction
In this paper, we address the problem by proposing a deep frame interpolation network to generate additional reference frames in coding scenarios.
UG$^{2+}$ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments
The UG$^{2+}$ challenge in IEEE CVPR 2019 aims to evoke a comprehensive discussion and exploration about how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios.
Bridging the Gap Between Computational Photography and Visual Recognition
From the observed results, it is evident that we are in the early days of building a bridge between computational photography and visual recognition, leaving many opportunities for innovation in this area.
Context-Aware Text-Based Binary Image Stylization and Synthesis
The stylization is then followed by a context-aware layout design algorithm, where cues for both seamlessness and aesthetics are employed to determine the optimal layout of the shape in the background.
Deep Retinex Decomposition for Low-Light Enhancement
Based on the decomposition, subsequent lightness enhancement is conducted on illumination by an enhancement network called Enhance-Net, and for joint denoising there is a denoising operation on reflectance.
Ranked #5 on
Low-Light Image Enhancement
on DICM
Progressive Spatial Recurrent Neural Network for Intra Prediction
With preceding pixels as the context, traditional intra prediction schemes generate linear predictions based on several predefined directions (i. e. modes) for blocks to be encoded.
A Group Variational Transformation Neural Network for Fractional Interpolation of Video Coding
Then a group variational transformation technique is used to transform a group of copied shared feature maps to samples at different sub-pixel positions.
Multimedia
DMCNN: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal
JPEG is one of the most commonly used standards among lossy image compression methods.
Erase or Fill? Deep Joint Recurrent Rain Removal and Reconstruction in Videos
In this paper, we address the problem of video rain removal by constructing deep recurrent convolutional networks.
Attentive Generative Adversarial Network for Raindrop Removal from a Single Image
This injection of visual attention to both generative and discriminative networks is the main contribution of this paper.
Dual Recovery Network with Online Compensation for Image Super-Resolution
In particular, we infer the HF information based on both the LR image and similar HR references which are retrieved online.
Robust LSTM-Autoencoders for Face De-Occlusion in the Wild
The first one, named multi-scale spatial LSTM encoder, reads facial patches of various scales sequentially to output a latent representation, and occlusion-robustness is achieved owing to the fact that the influence of occlusion is only upon some of the patches.
Deep Joint Rain Detection and Removal from a Single Image
Based on the first model, we develop a multi-task deep learning architecture that learns the binary rain streak map, the appearance of rain streaks, and the clean background, which is our ultimate output.
Photo Stylistic Brush: Robust Style Transfer via Superpixel-Based Bipartite Graph
With the rapid development of social network and multimedia technology, customized image and video stylization has been widely used for various social-media applications.
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution
To address this essentially ill-posed problem, we introduce a Deep Edge Guided REcurrent rEsidual~(DEGREE) network to progressively recover the high-frequency details.
