Low-light image enhancement (LLIE) is essential for enabling reliable nighttime visual perception and improving the performance of downstream vision tasks, including object detection and image segmentation. Under complex illumination conditions, low-light images often suffer from insufficient luminance, loss of structural details, and unstable color reproduction. Existing methods struggle to simultaneously restore luminance, texture, and color in a coherent manner. This paper proposes a Hierarchical Adaptive Interaction Modulation Network (HAIMNet) designed for LLIE. The proposed method decouples luminance and chromaticity in the Horizontal/Vertical-Intensity (HVI) color space, and enhances luminance-texture consistency through an inter-branch attention-modulation block (IAMB). Furthermore, a cross-branch gated affine fusion module (CGAF) is introduced to calibrate features between luminance and chromatic-structural representations, reduce color deviations, and enhance perceptual consistency. Extensive experiments on 11 public datasets demonstrate the effectiveness, robustness, and generalization capability of HAIMNet. The enhanced results exhibit high naturalness and stability under extremely dark and complex illumination conditions. Our code is available at: https://github.com/ZekeWang13/HAIMNet.

Due to substantial differences in appearance and movement between cartoon faces and real human faces, directly applying existing speech-driven speaker generation techniques to cartoon face images often fails to accurately capture key points, resulting in distorted expressions and animation artifacts. To address these challenges, this article proposes a speech-driven cartoon face animation generation model called CartoonTalk, which integrates 2D cartoon key points with 3D motion coefficients. The model comprises three modules: face annotation, motion extraction, and 3D facial rendering. The face annotation module first annotates a cartoon image, capturing its 2D key points. Subsequently, the motion extraction module derives 3D motion coefficients for head and facial expressions from speech data. Finally, the 3D facial rendering module uses these 2D and 3D features to generate the animated cartoon face. Quantitative and qualitative evaluations demonstrate that our model significantly outperforms existing approaches in both lip-sync accuracy and visual quality, establishing new state-of-the-art benchmarks on cartoon datasets.