Pseudo‐Siamese residual atrous pyramid network for multi‐focus image fusion

Abstract

Depth of field is one of the critical reasons to limit the richness of image information. Usually, in a scene with multiple targets, when the distance between each target and the lens is different, the clear scene image can be get within a certain distance range. This situation restricts the further image processing, such as semantic segmentation, object recognition and 3D reconstruction. Multi-focus image fusion uses two or more images focused on different targets to fuse scene information, which can solve this problem to a great extent. In general, two or more multi-focus images can cover almost all near/far targets. The fusion of more than two multi-focus images can be accomplished by cascading the fusion results of the previous two images and the next image to be processed many times. Therefore, the paper focus on the fusion of two multi-focus images. Inspired by this, new Pseudo-Siamese neural network with several residual atrous convolution pyramids with multi-level perception ability to perceive the multi-level features and consistency relations of multi-focus image pairs is proposed, and multi-layer residual blocks are used to fuse the extracted features. In this process, the residual of the groundtruth and the generated image will be learned. Finally, a fully focused image without blur will be generated. After several ablation experiments and comparison experiments with other methods, the results show that the performance of the method proposed in this paper is state-of-the-art, and overall better than other methods, which are advanced.