Noise-Controlled Image Restoration via Plug-and-Play Channel-Wise Modulation

Real-world weather conditions often involve combinations of rain, snow, haze, and poor illumination, yet image restoration networks are typically trained separately for individual degradations. Although these degradations may share common representation at certain feature levels, incorporating degradation-specific processing without redesigning the restoration backbone remains challenging. We propose \method, a plug-and-play conditioning framework for all-in-one image restoration that applies channel-wise affine modulation after complete backbone blocks. Fixed random Fourier features encode degradation categories, and independent lightweight controllers adapt features at different depths while preserving each block's internal computation. Multi-hot conditions represent mixed degradations; an optional jointly trained image-based predictor supplies soft conditions for label-free inference. The same interface supports convolutional and Transformer backbones with sequential or U-shaped structures. With known degradation labels, NC improves average single-weather PSNR over matched plain backbones by 0.47 dB for Restormer and 1.03 dB for PromptIR, and improves all seven CDD-11 weather mixtures for both backbones (+0.84 and +0.83 dB). The same modules accept soft conditions from a jointly trained CLIP predictor without modification: this blind variant improves the Transformer backbones by 0.82–1.40 dB on single weather and 0.17–0.30 dB on CDD-11 mixtures, and the predictor can be replaced without changing the restorer. Encoding ablations show a 0.76 dB gain from degradation-specific conditioning over constant control. These results support outside-block modulation as a plug-and-play conditioning interface for all-in-one restoration.