Underwater Image Enhancement Based on Convolutional Modulation and Spatial Collaboration
To address the issue of unclear textures and structures in underwater images due to light scattering and absorption effects in water,this paper proposes an underwater image enhancement algorithm based on Convolutional Modulation(CM)and Spatial Collaboration(SC).Using an encoder-decoder as the base network,the textural and structural features of underwater images are extracted using the shallow and deep networks of Reparameterization Visual Geometry Group(RepVGG).Initially,the feature dominative network transforms the underwater image features extracted from RepVGG into textural and structural features of various scales,which are then concatenated and fused with feature maps in the decoder.Subsequently,a CM module is employed within the encoder,where Depth Separable Convolution(DSConv)is implemented to simulate a self-attention mechanism to reduce the loss of image detail information and enhance feature extraction.Finally,Spatially Collaborative Convolution(SCConv)is performed in the decoder to process underwater features in the spatial dimension,preserving more positional information and enhancing the merged features.Experimental results indicate that this algorithm outperforms comparable algorithms in terms of visual perception and performance metrics,achieving a Peak Signal-to-Noise Ratio(PSNR)and Structural Similarity(SSIM)index as high as 23.446 5 dB and 0.894 6,respectively.The highest scores reached for Underwater Color Image Quality Evaluation(UCIQE)and Underwater Image Quality Measurement(UIQM)are 0.582 6 and 3.068 9,respectively,which further demonstrate the algorithm's effectiveness in enhancing textural and structural features of underwater images and providing superior visual perception.
image processingunderwater image enhancementConvolutional Modulation(CM)Spatial Collaboration(SC)encoding and decoding structure
万方数据
维普
