EvLIR: Learning Illumination Residuals from Ordered Events for Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2606.29430v1
• Date: Sun, 28 Jun 2026 14:47:49 GMT
• Title: EvLIR: Learning Illumination Residuals from Ordered Events for Low-Light Image Enhancement
• Abstract summary: EvLIR learns illumination residuals from ordered events for low-light image enhancement.<n>On SDE and SDSD indoor/outdoor benchmarks, EvLIR achieves the best result on eleven of twelve dataset-metric pairs.
• Score: 13.751247283539177
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Low-light image enhancement is severely ill-posed when the input frame contains missing structure, saturated noise, and weak local contrast. Event cameras provide asynchronous brightness-change observations with high temporal resolution, but prior works often treat voxel channels as an unordered or static feature stack before fusion, rather than explicitly modeling their within-window temporal evolution, weakening the temporal evidence that makes events useful. We propose EvLIR, a temporal-residual enhancement framework that learns illumination residuals from ordered events for low-light image enhancement. Given a low-light frame and its aligned event voxel, EvLIR preserves the ordered temporal bins of the event stream and introduces a Temporal Event Residual Module (TERM) to encode short-window event dynamics with a lightweight ConvGRU. The resulting temporal state is converted into a bounded illumination correction, which provides spatially adaptive photometric guidance for Retinex-style illumination estimation and subsequent reliability-aware image-event restoration. On SDE and SDSD indoor/outdoor benchmarks, EvLIR achieves the best result on eleven of twelve dataset-metric pairs, with average scores of 25.63~dB PSNR, 28.30~dB PSNR*, and 0.827 SSIM across the four benchmarks.

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