# Dense-FG: A Fusion GAN Model by Using Densely Connected Blocks to Fuse Infrared and Visible Images
## 2 Literature Survey  
### Research Status of Traditional Infrared and Visible Image Fusion  
From the perspective of fusion levels, image fusion can be divided into three levels from high to low: pixel level, feature level, and decision level. Pixel-level image fusion falls under the category of low-level image fusion [10], which involves extracting and computing features from processed images from multiple sources to create a fused image. Such fusion methods can retain more detailed information but require large amounts of data processing and have high demands on equipment.  
Spatial domain algorithms and transform domain algorithms are the most common pixel-level image fusion methods. Image fusion methods based on spatial domain include principal component analysis (PCA) [11], average gradient (AVG) [12], and others. The image fusion using PCA separately fuses the different components of the source images (mainly the first component). The image fusion using AVG takes the weighted average of corresponding pixels in the source images to form the fused pixel and generate a new fused image. This approach is straightforward and intuitive, but selecting relevant information can be challenging, and the fusion result is prone to instability.  
Transform domain image fusion methods include pyramid transform, wavelet transform, multi-scale geometric transform, and other similar techniques. Pyramid and wavelet transform-based image fusion typically involves decomposing the source views into multi-resolution architectures and merging the high-pass residual information and low-pass average details to reconstruct the ultimate fused image. Frequently employed techniques for decomposing and reconstructing data include the discrete wavelet transform (DWT) [13], Laplacian pyramid (LP) [14], filter subtract decimate pyramid (FSDP) [15], and contrast pyramid (CP) [16], as well as their respective improved variants. The above methods can only obtain decomposition coefficients in the horizontal, vertical, and diagonal directions during the source image decomposition process, which is not conducive to preserving detailed information in the fused image. Image fusion based on multi-scale geometric transforms can overcome this limitation, and common methods include curvelet[17], contourlet [18], NSCT [19], etc. Although such algorithms can effectively fuse source image information to some extent, specific implementation requires the manual setting of decomposition layers, low-frequency fusion strategies, high-frequency fusion strategies, and other parameters, making it more complex.  
Feature-level image fusion is categorized as a type of middle-level image fusion. This method collects features from the registered sources and accomplishes geometric alignment through parameter templates, statistical analysis, pattern correlation, and other methods [20]. The specific fusion algorithm and parameter selection need to be adjusted and optimized according to the specific application scenario.  
Decision-level image fusion belongs to the highest level of image fusion. This method extracts features from multiple input images and makes decisions separately, obtaining multiple decision results. Then, these decision results are combined to obtain the final fusion result. The advantage of decision-level image fusion is that it can use the decision results of different images for fusion, making the fusion results more comprehensive and accurate. However, its disadvantages are also quite obvious. It requires feature extraction and decision-making for each set of input images, which has a high computational complexity. At the same time, it is susceptible to the influence of feature-extraction and decision-making algorithms, therefore requiring high algorithm stability [21].  
In conclusion, traditional infrared and visible image fusion techniques possess their strengths and weaknesses, and suitable techniques should be selected based on the particular application scenario.