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Found 19 papers, 12 papers with code
A Diffusion-Based Framework for Occluded Object Movement
Seamlessly moving objects within a scene is a common requirement for image editing, but it is still a challenge for existing editing methods.
DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution
Large-scale pre-trained diffusion models are becoming increasingly popular in solving the Real-World Image Super-Resolution (Real-ISR) problem because of their rich generative priors.
Iterative Predictor-Critic Code Decoding for Real-World Image Dehazing
Apart from previous codebook-based methods that rely on one-shot decoding, our method utilizes high-quality codes obtained in the previous iteration to guide the prediction of the Code-Predictor in the subsequent iteration, improving code prediction accuracy and ensuring stable dehazing performance.
InterLCM: Low-Quality Images as Intermediate States of Latent Consistency Models for Effective Blind Face Restoration
Diffusion priors have been used for blind face restoration (BFR) by fine-tuning diffusion models (DMs) on restoration datasets to recover low-quality images.
FaceMe: Robust Blind Face Restoration with Personal Identification
In this paper, we propose a personalized face restoration method, FaceMe, based on a diffusion model.
Towards RAW Object Detection in Diverse Conditions
We find that sRGB pre-training constrains the potential of RAW object detection due to the domain gap between sRGB and RAW, prompting us to directly pre-train on the RAW domain.
Ranked #1 on
Object Detection
on AODRaw
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.
AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation
It is based on two essential designs.
RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors
(2) We propose a Real Image Dehazing network via high-quality Codebook Priors (RIDCP).
SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-Resolution
Without any bells and whistles, we show that our SRFormer achieves a 33. 86dB PSNR score on the Urban100 dataset, which is 0. 46dB higher than that of SwinIR but uses fewer parameters and computations.
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.
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.
Designing An Illumination-Aware Network for Deep Image Relighting
Lighting is a determining factor in photography that affects the style, expression of emotion, and even quality of images.
Image Harmonization by Matching Regional References
To achieve visual consistency in composite images, recent image harmonization methods typically summarize the appearance pattern of global background and apply it to the global foreground without location discrepancy.
Towards An End-to-End Framework for Flow-Guided Video Inpainting
Optical flow, which captures motion information across frames, is exploited in recent video inpainting methods through propagating pixels along its trajectories.
Ranked #2 on
Seeing Beyond the Visible
on KITTI360-EX
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.
FocusCut: Diving Into a Focus View in Interactive Segmentation
However, the global view makes the model lose focus from later clicks, and is not in line with user intentions.
Ranked #5 on
Interactive Segmentation
on SBD
