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We propose SICFLICS, a lightweight neural pipeline combining physics-based noise modeling and contrast-preserving neural denoising to improve low-light mobile photography. A calibrated noise priors module, per-pixel exposure fusion, and a small UNet with frequency-domain loss deliver visually pleasing results at <10 ms inference on mobile NPUs. On a collected 1,200-image low/normal-light paired dataset, SICFLICS achieves +4.2 dB PSNR and 0.31 higher SSIM over state-of-the-art mobile-friendly baselines and subjective preference of 78% in user studies.

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From one RAW, compute local gain map G(x) via a lightweight attention module predicting per-pixel exposure multipliers in [0.5, 4.0]. Synthesize multiple exposures RAW_k = clamp(RAW * G_k), then align via a fast bilateral flow and merge using learned weights to recover highlight and shadow detail.