2 research outputs found
A Symmetric Encoder-Decoder with Residual Block for Infrared and Visible Image Fusion
In computer vision and image processing tasks, image fusion has evolved into
an attractive research field. However, recent existing image fusion methods are
mostly built on pixel-level operations, which may produce unacceptable
artifacts and are time-consuming. In this paper, a symmetric encoder-decoder
with a residual block (SEDR) for infrared and visible image fusion is proposed.
For the training stage, the SEDR network is trained with a new dataset to
obtain a fixed feature extractor. For the fusion stage, first, the trained
model is utilized to extract the intermediate features and compensation
features of two source images. Then, extracted intermediate features are used
to generate two attention maps, which are multiplied to the input features for
refinement. In addition, the compensation features generated by the first two
convolutional layers are merged and passed to the corresponding deconvolutional
layers. At last, the refined features are fused for decoding to reconstruct the
final fused image. Experimental results demonstrate that the proposed fusion
method (named as SEDRFuse) outperforms the state-of-the-art fusion methods in
terms of both subjective and objective evaluations
Efficient and Interpretable Infrared and Visible Image Fusion Via Algorithm Unrolling
Infrared and visible image fusion expects to obtain images that highlight
thermal radiation information from infrared images and texture details from
visible images. In this paper, an interpretable deep network fusion model is
proposed. Initially, two optimization models are established to accomplish
two-scale decomposition, separating low-frequency base information and
high-frequency detail information from source images. The algorithm unrolling
that each iteration process is mapped to a convolutional neural network layer
to transfer the optimization steps into the trainable neural networks, is
implemented to solve the optimization models. In the test phase, the two
decomposition feature maps of base and detail are merged respectively by the
fusion layer, and then the decoder outputs the fusion image. Qualitative and
quantitative comparisons demonstrate the superiority of our model, which is
interpretable and can robustly generate fusion images containing highlight
targets and legible details, exceeding the state-of-the-art methods