April 4, 2024, 4:43 a.m. | Simon Welker, Henry N. Chapman, Timo Gerkmann

cs.LG updates on arXiv.org arxiv.org

arXiv:2211.06757v3 Announce Type: replace-cross
Abstract: In this work, we utilize the high-fidelity generation abilities of diffusion models to solve blind JPEG restoration at high compression levels. We propose an elegant modification of the forward stochastic differential equation of diffusion models to adapt them to this restoration task and name our method DriftRec. Comparing DriftRec against an $L_2$ regression baseline with the same network architecture and state-of-the-art techniques for JPEG restoration, we show that our approach can escape the tendency of …

abstract adapt arxiv blind compression cs.cv cs.lg differential differential equation diffusion diffusion models eess.iv equation fidelity solve stochastic them type work

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Snowflake Analytics Engineer - Technology Sector

@ Winning | Lisbon, Lisbon

Business Data Analyst

@ RideCo | Waterloo, Ontario, Canada

Senior Data Scientist, Payment Risk

@ Block | Boston, MA, United States

Research Scientist, Data Fusion (Climate TRACE)

@ WattTime | Remote

Technical Analyst (Data Analytics)

@ Contact Government Services | Fayetteville, AR