Towards Effective Noise Removal and Visual Enhancement Using CILA-SegNet for Color Casting and Illumination Distortions in Underwater Images
TL;DR
A deep learning based framework using MSRCC-Net by adding Adaptive Feature Decoupling Module (AFDM) and Hierarchical Feature Encoder (HFE) as novelty to correct color imbalance while simultaneously normalizing illumination variations and produces visually coherent, high-quality underwater images suitable for real-world operational use.
Abstract
Abstract - Underwater images frequently suffer from strong color casting and uneven lighting, both of which arise from light absorption, scattering, and suspended particles in the water. These distortions significantly hinder visual clarity and reduce the reliability of downstream analysis. Existing state of the art methods as Glad-Net and MSRCC-Net also suffer from color bias and uneven lighting and showed square effects in images recovery. This study proposes a deep learning based framework using MSRCC-Net by adding Adaptive Feature Decoupling Module (AFDM) and Hierarchical Feature Encoder(HFE) as novelty. This approach is designed to correct color imbalance while simultaneously normalizing illumination variations This proposed framework employs advanced neural architectures to extract global brightness inconsistencies and localized color deviations by proposing an approach named as CILA-SegNet (Color Casting and Illumination architectural segregation Network). It’s a combination of Multi-Scale Retinex with color correction module combined with CNN and transformer for axial-self attention module. Noise is removing by passing the preprocessed and augmented images to Adaptive Feature Decoupling Module(AFDM) for segregation in LAB channels and then to Hierarchical Feature Encoder(HFE) as Adaptive CLAHE and A,B soft color correction with MSRCC-Net model to improve the results. The initial results are promising, showing noticeable improvements in brightness, contrast, and image details with mean intensity growing from 116.35 to 119.54, contrast improving from 39.61 to 41.87, and entropy rising from 6.98 to 7.27 .This study obtained the noticeable reduction in computational cost with 11m parameters, 43.60g FLOPs and 0.150s latency. After results image grew slightly brighter and more detailed after CLAHE and produces visually coherent, high-quality underwater images suitable for real-world operational use.