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Found 19 papers, 17 papers with code
Taming Diffusion Models for Image Restoration: A Review
Diffusion models have achieved remarkable progress in generative modelling, particularly in enhancing image quality to conform to human preferences.
Conditional sampling within generative diffusion models
Generative diffusions are a powerful class of Monte Carlo samplers that leverage bridging Markov processes to approximate complex, high-dimensional distributions, such as those found in image processing and language models.
Equipping Diffusion Models with Differentiable Spatial Entropy for Low-Light Image Enhancement
In this work, we propose a novel method that shifts the focus from a deterministic pixel-by-pixel comparison to a statistical perspective, emphasizing the learning of distributions rather than individual pixel values.
Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models
Though diffusion models have been successfully applied to various image restoration (IR) tasks, their performance is sensitive to the choice of training datasets.
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning
We show that such an SDE has a solution that we can use to calculate the log probability of the policy, yielding an entropy regularizer that improves the exploration of offline datasets.
Controlling Vision-Language Models for Multi-Task Image Restoration
In this paper, we present a degradation-aware vision-language model (DA-CLIP) to better transfer pretrained vision-language models to low-level vision tasks as a multi-task framework for image restoration.
Ranked #2 on
Single Image Deraining
on Rain100H
RL-I2IT: Image-to-Image Translation with Deep Reinforcement Learning
The key feature in the RL-I2IT framework is to decompose a monolithic learning process into small steps with a lightweight model to progressively transform a source image successively to a target image.
Lens-to-lens bokeh effect transformation. NTIRE 2023 challenge report
We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge.
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models
This work aims to improve the applicability of diffusion models in realistic image restoration.
Image Restoration with Mean-Reverting Stochastic Differential Equations
This paper presents a stochastic differential equation (SDE) approach for general-purpose image restoration.
Ranked #3 on
Single Image Deraining
on Rain100H
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.
Fast Nearest Convolution for Real-Time Efficient Image Super-Resolution
The proposed nearest convolution has the same performance as the nearest upsampling but is much faster and more suitable for Android NNAPI.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment
To overcome the challenges in BurstSR, we propose a Burst Super-Resolution Transformer (BSRT), which can significantly improve the capability of extracting inter-frame information and reconstruction.
Ranked #1 on
Burst Image Super-Resolution
on SyntheticBurst
Deep Constrained Least Squares for Blind Image Super-Resolution
In this paper, we tackle the problem of blind image super-resolution(SR) with a reformulated degradation model and two novel modules.
Ranked #1 on
Blind Super-Resolution
on DIV2KRK - 2x upscaling
Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration
We evaluate our method on several 2D and 3D medical image datasets, some of which contain large deformations.
Stochastic Actor-Executor-Critic for Image-to-Image Translation
Training a model-free deep reinforcement learning model to solve image-to-image translation is difficult since it involves high-dimensional continuous state and action spaces.
EBSR: Feature Enhanced Burst Super-Resolution With Deformable Alignment
We propose a novel architecture to handle the problem of multi-frame super-resolution (MFSR).
Ranked #2 on
Burst Image Super-Resolution
on SyntheticBurst
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
This paper reviews the NTIRE2021 challenge on burst super-resolution.
