NiDNeXt: cross-channel interactive and multi-scale fusion for nighttime dehazing

Due to severe haze pollution and color attenuation, nighttime dehazing is an extremely challenging problem. Current research often neglects the impact of multiple unevenly illuminated active color light sources in night scenes, which may lead to halo, mist, glow and noise with localized, coupled, inconsistent frequency and unnatural characteristics. Additionally, after fog removal, night images may suffer from color distortion, detail loss, and texture blur. To address these issues, we present NiDNeXt, a nighttime dehazing network that integrates multi-scale progressive fusion (MSPF), cross-channel interactive attention (CCIA), and Fourier dynamic frequency filter (FDFF) modules. MSPF fuses spatial and frequency information locally, removing haze, glow, and noise in a coarse-to-fine manner across multiple scales while regenerating image details. CCIA allows channels to selectively absorb valuable information from adjacent channels using learned dynamic weights, enhancing the extraction of clear elements, color channel compensation, and texture restoration. FDFF, utilized within MSPF and CCIA, exploits dual-domain difference information to improve haze removal, glow elimination, texture restoration, and color correction. Our experiments on public benchmark datasets confirm the effectiveness and superiority of NiDNeXt over state-of-the-art methods. Our code will be made public at https://github.com/AQUAY16/NiDNeXt.