#image-editing

3 articles

01 ·★ MEMBER·PAPER·6 min read Paper Explained: Mi-Ripple — Diagnose First, Then Repair the Ripple That Iterative AI Editing Leaves Behind Feed an AI-edited image back in as the reference for the next edit, and grid-like and grainy texture — digital ripple — starts to build up. Mi-Ripple first tells apart a lattice you can cut out in the frequency domain from grain tangled up with the picture, then picks either a notch filter or regeneration from a cleaned reference. 02 ·★ MEMBER·PAPER·8 min read Paper Explained: SenseNova-U1.5 — One Model That Both Sees and Draws, With No Encoder and No VAE SenseNova-U1.5 is an 8B unified model that learns straight from pixels and words, with no vision encoder for understanding and no VAE for generation. We walk through its seam-free spatial decoder and its 'train specialists separately, then distill them into one' post-training, following the paper itself. 03 ·★ MEMBER·PAPER·11 min read Paper Explained: VDiff-Bench — Even Frontier MLLMs Miss the Low-Level Changes in a Spot-the-Difference Puzzle VDiff-Bench asks a multimodal LLM what changed between two near-identical images, as a four-way multiple choice. We walk through how its 1,756 questions across 10 change categories were built, what 11 models scored, why several of them answer 'no difference' when noise or texture changed, and what this means if you use an MLLM to grade image edits.