关键词: 图像处理, 特征提取, 多尺度特征, 深层卷积, 强化色彩

Abstract: To address problems caused by deep-sea imaging, such as color distortion, low contrast, and blurred structures in underwater images, a Multiscale Parallel Enhancement Network (MPENet) was proposed from the perspectives of spatial, channel, and three-dimensional features. Image information was fed in parallel into a multi-dimensional feature extraction network and an encoder. A multiscale feature refinement module was introduced into the feature extraction network to further process extracted features, allowing the network to learn information at different scales more accurately. A multi-dimensional color enhancement module was incorporated into the encoder to enhance image details and colors. An adaptive enhancement network was then designed to further refine feature representations and fuse multi-level features. Finally, a decoder was used to generate the enhanced image. The proposed method demonstrates outstanding performance on public datasets. It achieves a peak signal-to-noise ratio (PSNR) of up to 24.8651 and a structural similarity index measure (SSIM) of 0.8954, representing improvements of 1.5806 and 0.0398 over HFM, respectively. Underwater color image quality evaluation (UCIQE) and underwater image quality measure (UIQM) reach 0.5931 and 3.1028, surpassing HFM by 0.0384 and 0.1514, respectively. Experimental results show that MPENet effectively improves underwater visual quality.

Key words: image processing, feature extraction, multiscale feature, deep convolution, intensified color