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版本更新 · v3arXiv:2606.29845

Bricker to BRACE: A Bracket Exposure RAW Dataset and Restoration Model for Flicker-Banding

Zihan Zhou, Libo Zhu, Jue Gong, Zhiyi Zhou, Jiezhang Cao, Yong Guo, Yulun Zhang

原文摘要

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. To address this, we conduct a systematic analysis of complex FB morphologies and reveal their significant variation across exposure settings, motivating a multi-frame bracketed RAW restoration paradigm. We construct Bricker, a synthetic-real bracketed RAW dataset built via ray-tracing-based physical simulation and an automated multi-exposure capture tool. We further propose BRACE: Bracketed RAW Flicker-Banding Removal, a multi-frame restoration model that utilizes a frequency-aware banding prior and a multi-scale spatial cross-attention modulator (MSCAM) for cross-exposure spatial fusion. We also introduce the Stripe Frequency Consistency (SFC) metric to evaluate banding removal. Experiments demonstrate state-of-the-art performance on both synthetic and real benchmarks. Our dataset and code are available at: https://github.com/ZZH-qwq/BRACE

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05168

DSL-Bench: Evaluating and Explaining LLM Code Generation Across Domain-Specific Languages

Zihan Zhou, Mingxi Ye, Yan Gao, Guipeng Wei, Zibin Zheng

原文摘要

Large language models (LLMs) are increasingly used in software-engineering workflows, but code-generation benchmarks largely focus on general-purpose languages. Domain-specific languages (DSLs) have distinct syntax, semantics, and validation requirements, making results from individual DSL benchmarks difficult to compare. We present DSL-Bench, an executable benchmark and analysis framework for cross-DSL code generation. Starting from 13,045 DSL-related papers, LLM-assisted open card sorting identifies 18 representative application domains. For each domain, we select an evaluable DSL and provide a syntax checker and task-level semantic oracle. Evaluating 7 LLMs on 18 DSLs, we observe mean syntax and semantic pass rates of 31.37% and 11.64%, respectively, revealing a substantial gap between syntax acceptance and semantic correctness. A variance decomposition attributes 76.99% of semantic pass-rate variation to differences among DSLs and 7.86% to differences among models. Among four DSL-level factors, Knowledge Demand has the strongest negative association with semantic correctness (r = -0.508). We evaluate prompt engineering across all 18 DSLs and study five improvement methods on five targeted DSLs. Prompt engineering yields uneven gains; RAG-Refinement, the strongest targeted method, improves semantic correctness by 16.29 percentage points on average over matched baselines. We further derive capability profiles and DSL-Profiler summaries for interpreting data-scarce DSLs. These results highlight toolchain-specific semantic bottlenecks and the need to evaluate DSL code generation beyond syntax acceptance.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2509.24644

RIFLE: Removal of Image Flicker-Banding via Latent Diffusion Enhancement

Libo Zhu, Zihan Zhou, Xiaoyang Liu, Weihang Zhang, Keyu Shi, Yifan Fu, Yulun Zhang

原文摘要

Capturing screens is now routine in our everyday lives. But the photographs of emissive displays are often influenced by the flicker-banding (FB), which is alternating bright-dark stripes that arise from temporal aliasing between a camera's rolling-shutter readout and the display's brightness modulation. Unlike moire degradation, which has been extensively studied, the FB remains underexplored despite its frequent and severe impact on readability and perceived quality. We formulate FB removal as a dedicated restoration task and introduce Removal of Image Flicker-Banding via Latent Diffusion Enhancement, RIFLE, a diffusion-based framework designed to remove FB while preserving fine details. We propose the flicker-banding prior estimator (FPE) that predicts key banding attributes and injects it into the restoration network. Additionally, Masked Loss (ML) is proposed to concentrate supervision on banded regions without sacrificing global fidelity. To overcome data scarcity, we provide a simulation pipeline that synthesizes FB in the luminance domain with stochastic jitter in banding angle, banding spacing, and banding width. Feathered boundaries and sensor noise are also applied for a more realistic simulation. For evaluation, we collect a paired real-world FB dataset with pixel-aligned banding-free references captured via long exposure. Across quantitative metrics and visual comparisons on our real-world dataset, RIFLE consistently outperforms recent image reconstruction baselines from mild to severe flicker-banding. To the best of our knowledge, it is the first work to research the simulation and removal of FB. Our work establishes a great foundation for subsequent research in both the dataset construction and the removal model design. Our dataset and code will be released soon.

cs.CV

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arXiv ↗PDF ↗