Exposure correction method based on multi-level pyramid information fusion
To address underexposure and overexposure in images,a multi-level information fusion exposure correction network based on the Laplacian pyramid structure was developed.Each network level adopted a U-Net-like encoder-decoder architecture in its correction module.A multi-scale convolutional encoder based on ConvNeXt-tiny was designed as the primary feature extraction unit to enhance feature extraction ability while reducing the mod-el's parameter count.To tackle the issue of checkerboard artifacts arising during image up-sampling,a dual-path up-sampling module combining bilinear interpolation and sub-pixel convolution was proposed.The network demonstrated effective results in both quantitative and qualitative validations on a large-scale exposure correction dataset.Dowel positioning experiments showed significant improvements in feature repeatability,positioning accura-cy,and stability at varying contrast thresholds when the network was applied to image enhancement.
information fusionexposure correctionmulti-scale convolutional encoderdual-path up-sampling
万方数据
维普
