Sicflics

Total loss = λ1 L1(image) + λ2 L_perceptual(VGG) + λ3 L_Fourier(band) + λ4 L_noise_KL + λ5 L_confidence. Fourier term penalizes energy mismatch in mid-to-high bands to preserve texture.

While the term "Sicflic" is a product of 2010s internet culture, the roots run deep into 1970s New American Cinema. sicflics

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. Total loss = λ1 L1(image) + λ2 L_perceptual(VGG)

Helpful feature: Sci‑Fi concept tracker If you want to identify a true Sicflic

If you want to identify a true Sicflic, look for these three visual hallmarks: