Bricker to BRACE: A Bracket Exposure RAW Dataset and Restoration Model for Flicker-Banding
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Flicker-banding (FB), arises from temporal aliasing between a camera's rolling shutter and a display's brightness modulation, degrading screen-captured image readability with color shifts and jagged patterns. Existing single-frame methods with simplified parametric stripe models cannot reliably distinguish these artifacts from genuine texture.
arXiv:2509.24644v5 Announce Type: replace Abstract: Capturing screens is now routine in our everyday lives. But the photographs of emissive displays are often influenced by the flicker-banding (FB),...
arXiv:2609.27274v1 Announce Type: new Abstract: Due to the limited dynamic range of conventional image sensors, captured low dynamic range (LDR) video often suffers from highlight clipping and shadow...
Due to the limited dynamic range of conventional image sensors, captured low dynamic range (LDR) video often suffers from highlight clipping and shadow detail loss, making high-quality high dynamic ra...
arXiv:2512. 04390v2 Announce Type: replace-cross Abstract: Joint video super-resolution and deblurring (VSRDB) requires both efficient long-range temporal modeling and robustness to frame-wise exposure-duration variation, which changes the extent of motion blur across video frames.
The paper introduces OcuBench, a comprehensive benchmark for eyeglass reflection removal that includes 10,280 synthetic pairs, 732 real-input pseudo-pairs, and 458 real-world test images, enabling both paired evaluation and assessment beyond generated supervision. It also proposes OcuFlow, an ocular-adaptive pixel MeanFlow framework that uses geometry-adaptive representation and one-step pMF to focus on reflection-obscured ocular regions while preserving native-resolution details. Experiments show OcuFlow consistently outperforms baselines in reflection removal quality, ocular fidelity, and efficiency, achieving 67.32% of selections in a blind user study, six times the next-best share.