Core Concepts
The author proposes a method that is type and severity aware for blind all-in-one weather removal, utilizing Contrastive Loss (CL) and Marginal Quality Ranking Loss (MQRL) to guide the model in extracting representative weather information effectively.
Abstract
The content discusses a novel approach for image restoration in adverse weather conditions. The proposed method, UtilityIR, outperforms existing techniques subjectively and objectively by addressing degradation type and severity simultaneously. By modulating restoration levels and handling combined multiple degradations, the model demonstrates superior performance.
The paper highlights the challenges faced in all-in-one adverse weather removal, emphasizing the importance of integrating different operations suitable for various weather degradations. It introduces UtilityIR as a solution that can restore images degraded by different adverse weather conditions efficiently.
The study showcases the effectiveness of CL and MQRL in guiding the extraction of representative weather information, leading to improved restoration results. Through experiments and comparisons with state-of-the-art methods, UtilityIR proves to be a promising approach for image restoration in adverse weather conditions.
Key points include progressive restoration capabilities, restoration level modulation using latent space manipulation, and successful restoration of combined multiple weather images. The paper concludes by acknowledging limitations and outlining future research directions.
Stats
Proposed method named UtilityIR outperforms existing techniques subjectively and objectively.
Model achieves state-of-the-art performance on different weather removal tasks.
Less parameters are required compared to other blind all-in-one methods.