TY - JOUR
T1 - U-TransCNN
T2 - A U-shape transformer-CNN fusion model for underwater image enhancement
AU - Haiyang, Yao
AU - Ruige, Guo
AU - Zhongda, Zhao
AU - Yuzhang, Zang
AU - Xiaobo, Zhao
AU - Tao, Lei
AU - Haiyan, Wang
N1 - Publisher Copyright:
© 2025 Elsevier B.V.
PY - 2025/7
Y1 - 2025/7
N2 - Underwater imaging faces significant challenges due to nonuniform optical absorption and scattering, resulting in visual quality issues like color distortion, contrast reduction, and image blurring. These factors hinder the accurate capture and clear depiction of underwater imagery. To address these complexities, we propose U-TransCNN, a U-shape Transformer- Convolutional Neural Networks (CNN) model, designed to enhance underwater images by integrating the strengths of CNNs and Transformers. The core of U-TransCNN is the Global-Detail Feature Synchronization Fusion Module. This innovative component enhances global color and contrast while meticulously preserving the intricate texture details, ensuring that both macroscopic and microscopic aspects of the image are enhanced in unison. Then we design the Multiscale Detail Fusion Block to aggregate a richer spectrum of feature information using a variety of convolution kernels. Furthermore, our optimization strategy is augmented with a joint loss function, adynamic approach allowing the model to assign varying weights to the loss associated with different pixel points, depending on their loss magnitude. Six experiments (including reference and non-reference) on three public underwater datasets confirm that U-TransCNN comprehensively surpasses other contemporary state-of-the-art deep learning algorithms, demonstrating marked improvement in visualization quality and quantization parameters of underwater images. Our code is available at https://github.com/GuoRuige/UTransCNN.
AB - Underwater imaging faces significant challenges due to nonuniform optical absorption and scattering, resulting in visual quality issues like color distortion, contrast reduction, and image blurring. These factors hinder the accurate capture and clear depiction of underwater imagery. To address these complexities, we propose U-TransCNN, a U-shape Transformer- Convolutional Neural Networks (CNN) model, designed to enhance underwater images by integrating the strengths of CNNs and Transformers. The core of U-TransCNN is the Global-Detail Feature Synchronization Fusion Module. This innovative component enhances global color and contrast while meticulously preserving the intricate texture details, ensuring that both macroscopic and microscopic aspects of the image are enhanced in unison. Then we design the Multiscale Detail Fusion Block to aggregate a richer spectrum of feature information using a variety of convolution kernels. Furthermore, our optimization strategy is augmented with a joint loss function, adynamic approach allowing the model to assign varying weights to the loss associated with different pixel points, depending on their loss magnitude. Six experiments (including reference and non-reference) on three public underwater datasets confirm that U-TransCNN comprehensively surpasses other contemporary state-of-the-art deep learning algorithms, demonstrating marked improvement in visualization quality and quantization parameters of underwater images. Our code is available at https://github.com/GuoRuige/UTransCNN.
KW - CNN
KW - Feature fusion
KW - Transformer
KW - Underwater image enhancement
UR - http://www.scopus.com/inward/record.url?scp=105001480655&partnerID=8YFLogxK
U2 - 10.1016/j.displa.2025.103047
DO - 10.1016/j.displa.2025.103047
M3 - Journal article
AN - SCOPUS:105001480655
SN - 0141-9382
VL - 88
JO - Displays
JF - Displays
M1 - 103047
ER -