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Found 42 papers, 19 papers with code
Wavelet-Based Dual-Branch Network for Image Demoiréing
When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality.
More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning
Since those regularization strategies are mostly associated with classifier outputs, we propose a MUlti-Classifier (MUC) incremental learning paradigm that integrates an ensemble of auxiliary classifiers to estimate more effective regularization constraints.
Reconstructing the Noise Variance Manifold for Image Denoising
Deep Convolutional Neural Networks (CNNs) have been successfully used in many low-level vision problems like image denoising.
Adaptive Multi-Modal Control of Digital Human Hand Synthesis Using a Region-Aware Cycle Loss
Our experimental evaluations demonstrate the effectiveness of our proposed approach in improving the quality of digital human pose generation using diffusion models, especially the quality of the hand region.
Improving Interpretability and Robustness for the Detection of AI-Generated Images
In this work, we focus on the robustness of AI-generated image (AIGI) detectors.
CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentation
Convolutional Neural Networks (CNNs) and Transformer-based self-attention models have become the standard for medical image segmentation.
CUE-Net: Violence Detection Video Analytics with Spatial Cropping, Enhanced UniformerV2 and Modified Efficient Additive Attention
In this paper we introduce CUE-Net, a novel architecture designed for automated violence detection in video surveillance.
Multi-view Cardiac Image Segmentation via Trans-Dimensional Priors
We evaluate our proposed method on the Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI (M&Ms-2) dataset, where our method outperforms state-of-the-art methods in segmenting cardiac regions of interest in both short-axis and long-axis images.
Machine Learning Techniques for Source Localisation in Elastic Media
The blood flow in the artery is disturbed and imposes oscillatory stresses on the artery wall.
RAVE: Residual Vector Embedding for CLIP-Guided Backlit Image Enhancement
Instead, based on CLIP embeddings of backlit and well-lit images from training data, we compute the residual vector in the embedding space as a simple difference between the mean embeddings of the well-lit and backlit images.
RoGUENeRF: A Robust Geometry-Consistent Universal Enhancer for NeRF
Conversely, existing 3D enhancers are able to transfer detail from nearby training images in a generalizable manner, but suffer from inaccurate camera calibration and can propagate errors from the geometry into rendered images.
MOAB: Multi-Modal Outer Arithmetic Block For Fusion Of Histopathological Images And Genetic Data For Brain Tumor Grading
We propose a novel Multi-modal Outer Arithmetic Block (MOAB) based on arithmetic operations to combine latent representations of the different modalities for predicting the tumor grade (Grade \rom{2}, \rom{3} and \rom{4}).
FOAA: Flattened Outer Arithmetic Attention For Multimodal Tumor Classification
Fusion of multimodal healthcare data holds great promise to provide a holistic view of a patient's health, taking advantage of the complementarity of different modalities while leveraging their correlation.
Crop and Couple: cardiac image segmentation using interlinked specialist networks
Diagnosis of cardiovascular disease using automated methods often relies on the critical task of cardiac image segmentation.
MUSTANG: Multi-Stain Self-Attention Graph Multiple Instance Learning Pipeline for Histopathology Whole Slide Images
Our approach is highly modular and can easily be modified to suit different clinical datasets, as it only requires a patient-level label without annotations and accepts WSI sets of different sizes, as the graphs can be of varying sizes and structures.
Automated segmentation of rheumatoid arthritis immunohistochemistry stained synovial tissue
The model obtains a DICE score of 0. 865 and successfully segments different types of IHC staining, as well as dealing with variance in colours, intensity and common WSIs artefacts from the different clinical centres.
Graph Neural Networks in Vision-Language Image Understanding: A Survey
2D image understanding is a complex problem within computer vision, but it holds the key to providing human-level scene comprehension.
Vector Quantized Semantic Communication System
Although analog semantic communication systems have received considerable attention in the literature, there is less work on digital semantic communication systems.
FlexHDR: Modelling Alignment and Exposure Uncertainties for Flexible HDR Imaging
We introduce a strategy that learns to jointly align and assess the alignment and exposure reliability using an HDR-aware, uncertainty-driven attention map that robustly merges the frames into a single high quality HDR image.
Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs
In this paper, we propose a self-adaptive learning method for demoiréing a high-frequency image, with the help of an additional defocused moiré-free blur image.
Learning to Sample the Most Useful Training Patches from Images
Some image restoration tasks like demosaicing require difficult training samples to learn effective models.
Self-Adaptively Learning to Demoire from Focused and Defocused Image Pairs
In this paper, we propose a self-adaptive learning method for demoireing a high-frequency image, with the help of an additional defocused moire-free blur image.
Diagnosing and Preventing Instabilities in Recurrent Video Processing
Recurrent models are a popular choice for video enhancement tasks such as video denoising or super-resolution.
Video Super-resolution with Temporal Group Attention
Video super-resolution, which aims at producing a high-resolution video from its corresponding low-resolution version, has recently drawn increasing attention.
Low Light Video Enhancement using Synthetic Data Produced with an Intermediate Domain Mapping
Advances in low-light video RAW-to-RGB translation are opening up the possibility of fast low-light imaging on commodity devices (e. g. smartphone cameras) without the need for a tripod.
Image and Video Processing
Wavelet-Based Dual-Branch Network for Image Demoireing
When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality.
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
NTIRE 2020 Challenge on Image Demoireing: Methods and Results
Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image.
Image Demoireing with Learnable Bandpass Filters
Image demoireing is a multi-faceted image restoration task involving both texture and color restoration.
Ranked #2 on
Image Enhancement
on TIP 2018
(using extra training data)
DeepLPF: Deep Local Parametric Filters for Image Enhancement
We introduce a deep neural network, dubbed Deep Local Parametric Filters (DeepLPF), which regresses the parameters of these spatially localized filters that are then automatically applied to enhance the image.
Ranked #8 on
Image Enhancement
on MIT-Adobe 5k
(SSIM on proRGB metric)
Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem
This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device, with desirable properties regarding scalability and privacy constraints.
A Multi-Hypothesis Approach to Color Constancy
Firstly, we select a set of candidate scene illuminants in a data-driven fashion and apply them to a target image to generate of set of corrected images.
Reconstructing the Noise Manifold for Image Denoising
Deep Convolutional Neural Networks (CNNs) have been successfully used in many low-level vision problems like image denoising.
CURL: Neural Curve Layers for Global Image Enhancement
We present a novel approach to adjust global image properties such as colour, saturation, and luminance using human-interpretable image enhancement curves, inspired by the Photoshop curves tool.
Ranked #4 on
Photo Retouching
on MIT-Adobe 5k
Pixel Adaptive Filtering Units
Next, we employ PAFU in deep neural networks as a replacement of standard convolutional layers to enhance the original architectures with spatially varying computations to achieve considerable performance improvements.
AIM 2019 Challenge on Image Demoireing: Methods and Results
A new dataset, called LCDMoire was created for this challenge, and consists of 10, 200 synthetically generated image pairs (moire and clean ground truth).
AIM 2019 Challenge on Image Demoireing: Dataset and Study
In addition to describing the dataset and its creation, this paper also reviews the challenge tracks, competition, and results, the latter summarizing the current state-of-the-art on this dataset.
SteReFo: Efficient Image Refocusing with Stereo Vision
Whether to attract viewer attention to a particular object, give the impression of depth or simply reproduce human-like scene perception, shallow depth of field images are used extensively by professional and amateur photographers alike.
A continual learning survey: Defying forgetting in classification tasks
Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase.
NODE: Extreme Low Light Raw Image Denoising using a Noise Decomposition Network
In this paper, we propose a multi-task deep neural network called Noise Decomposition (NODE) that explicitly and separately estimates defective pixel noise, in conjunction with Gaussian and Poisson noise, to denoise an extreme low light image.
Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem
In this work, we propose a new approach that affords fast adaptation to previously unseen cameras, and robustness to changes in capture device by leveraging annotated samples across different cameras and datasets.
Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models
Magnetic Resonance Spectroscopy (MRS) provides valuable information to help with the identification and understanding of brain tumors, yet MRS is not a widely available medical imaging modality.
