Multi-focus image fusion plays an important role in the application of computer vision. In the process of image fusion, there may be blurring and information loss, so it is our goal to obtain high-definition and information-rich fusion images. In this paper, a novel multi-focus image fusion method via local energy and sparse representation in the shearlet domain is proposed. The source images are decomposed into low- and high-frequency sub-bands according to the shearlet transform. The low-frequency sub-bands are fused by sparse representation, and the high-frequency sub-bands are fused by local energy. The inverse shearlet transform is used to reconstruct the fused image. The Lytro dataset with 20 pairs of images is used to verify the proposed method, and 8 state-of-the-art fusion methods and 8 metrics are used for comparison. According to the experimental results, our method can generate good performance for multi-focus image fusion.
Super-resolution (SR) images based on deep networks have achieved great accomplishments in recent years, but the large number of parameters that come with them are not conducive to use in equipment with limited capabilities in real life. Therefore, we propose a lightweight feature distillation and enhancement network (FDENet). Specifically, we propose a feature distillation and enhancement block (FDEB), which contains two parts: a feature-distillation part and a feature-enhancement part. Firstly, the feature-distillation part uses the stepwise distillation operation to extract the layered feature, and here we use the proposed stepwise fusion mechanism (SFM) to fuse the retained features after stepwise distillation to promote information flow and use the shallow pixel attention block (SRAB) to extract information. Secondly, we use the feature-enhancement part to enhance the extracted features. The feature-enhancement part is composed of well-designed bilateral bands. The upper sideband is used to enhance the features, and the lower sideband is used to extract the complex background information of remote sensing images. Finally, we fuse the features of the upper and lower sidebands to enhance the expression ability of the features. A large number of experiments show that the proposed FDENet both produces less parameters and performs better than most existing advanced models.
To solve problems of brightness and detail information loss in infrared and visible image fusion, an effective infrared and visible image fusion method using rolling guidance filtering and gradient saliency map is proposed in this paper. The rolling guidance filtering is used to decompose the input images into approximate layers and residual layers; the energy attribute fusion model is used to fuse the approximate layers; the gradient saliency map is introduced and the corresponding weight matrices are constructed to perform on residual layers. The fusion image is generated by reconstructing the fused approximate layer sub-image and residual layer sub-images. Experimental results demonstrate the superiority of the proposed infrared and visible image fusion method.
Due to the contrast of X-ray images being low, significant elements including organs, bones, and nodules are very difficult to identify, so contrast enhancement is necessary. In this paper, an X-ray image enhancement algorithm based on adaptive gradient domain guided image filtering is proposed. The amplification factor in the gradient domain guided image filtering needs to be set manually; it needs to constantly adjust the parameters to achieve the best enhancement effect, and this also increases the computational complexity. In order to solve this problem, an adaptive amplification factor is defined in this paper, and the proposed algorithm is applied to the X-ray image enhancement. Experimental results demonstrate that the proposed method is superior to state-of-the art algorithms in terms of detail enhancement and edge-preserving.
In recent years, with the increasingly serious problems of resource shortage and environmental pollution, the exploration and development of underwater clean energy were particularly important. At the same time, abundant underwater resources and species have attracted a large number of scientists to carry out research on underwater-related tasks. Due to the diversity and complexity of underwater environments, it is difficult to perform related vision tasks, such as underwater target detection and capture. The development of digital image technology has been relatively mature, and it has been applied in many fields and achieved remarkable results, but the research on underwater image processing technology is rarely effective. The underwater environment is much more complicated than that on land, and there is no light source underwater. Underwater imaging systems must rely on artificial light sources for illumination. When light travels through water, it is severely attenuated by water absorption, reflection, and scattering. The collected underwater images inevitably have problems such as limited visible range, blur, low contrast, uneven illumination, incoherent colors, and noise. The purpose of image enhancement is to improve or solve one or more of the above problems in a targeted manner. Therefore, underwater image enhancement technology has become one of the key contents of underwater image processing technology research. In this paper, we proposed a conditional generative adversarial network model based on attention U-Net which contains an attention gate mechanism that could filter invalid feature information and capture contour, local texture, and style information effectively. Furthermore, we formulate an objective function through three different loss functions, which can evaluate image quality from global content, color, and structural information. Finally, we performed end-to-end training on the UIEB real-world underwater image dataset. The comparison experiments show that our method outperforms all comparative methods, the ablation experiments show that the loss function proposed in this paper outperforms a single loss function, and finally, the generalizability of our method is verified by executing on two different datasets, UIEB and EUVP.
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