Search Results for author:
Found 5 papers, 1 papers with code
Towards Extreme Image Compression with Latent Feature Guidance and Diffusion Prior
To address this problem, we propose a novel extreme image compression framework that combines compressive VAEs and pre-trained text-to-image diffusion models in an end-to-end manner.
Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey
This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs.
Traditional Transformation Theory Guided Model for Learned Image Compression
Recently, many deep image compression methods have been proposed and achieved remarkable performance.
Depth Super-Resolution from Explicit and Implicit High-Frequency Features
We propose a novel multi-stage depth super-resolution network, which progressively reconstructs high-resolution depth maps from explicit and implicit high-frequency features.
Rethinking Blur Synthesis for Deep Real-World Image Deblurring
In this paper, we examine the problem of real-world image deblurring and take into account two key factors for improving the performance of the deep image deblurring model, namely, training data synthesis and network architecture design.
