This paper introduces an advanced Underwater Image Enhancement (UIE) framework that integrates multi-level color correction with multi-scale restoration to address the challenges of color distortion and image quality degradation in underwater environments caused by complex lighting variations and haze effects. Central to the framework is the Multi-Level Color Correction model with Adaptive Factor (MLCC-AF), which leverages the principle of color constancy to estimate light color across multiple levels. The model dynamically adjusts global color balance, corrects local highlight regions, and redistributes channel color energy through an adaptive correction factor, effectively mitigating color deviations and significantly enhancing color fidelity and visual quality. Complementing this, the Multi-scale Joint Restoration Network (MJRN) and Residual-based Detail Enhancement Network (RDEN) are proposed to tackle haze effects and recover lost details. MJRN optimizes the dehazing process through joint parameter estimation, while RDEN adaptively enhances critical image features, ensuring superior clarity and detail preservation. Extensive experiments conducted on both reference and non-reference underwater image datasets demonstrate that the proposed method consistently outperforms existing state-of-the-art approaches in terms of color correction, contrast enhancement, and detail restoration. The results underline the method’s efficiency and robustness, offering a promising solution for UIE applications.

In the domain of Person Re-IDentification (ReID) tasks, images of pedestrians often exhibit noticeable color variations due to diverse environmental conditions. Notably, the accuracy of retrieval results improves when some color information is disregarded. To mitigate the adverse effects of color variations on recognition performance, this research introduces the Random Color Dropout (RCD) data augmentation strategy, employing the Local Aggregated Grayscale Transformation (LAGT) as its foundation. The proposed strategy enhances model’s robustness against color deviations by equitably adjusting transformation weights across the RGB image’s three channels. Additionally, to minimize overall model parameters, we employ the lightweight and efficient OSNet as the backbone. Recognizing the network’s limited attention to feature details and the interference of intricate scenes on recognition, we incorporate a complementary cascade-type Self-Attention Module (SAM). This module effectively consolidates spatial and channel information within the feature map, ameliorating information deficits in features and augmenting their discriminative properties. On benchmark datasets, Market1501 and DukeMTMC-reID, our method attains performance metrics of 95.5% and 89.2% in Rank-1, and 87.7% and 77.2% in mAP, respectively. These results underscore the superior performance of the proposed method compared to the prevailing mainstream algorithms.