A New Robust Multi focus image fusion Method

In today's digital era, multi focus picture fusion is a critical problem in the field of computational image processing. In the field of fusion information, multi-focus picture fusion has emerged as a significant research subject. The primary objective of multi focus image fusion is to merge graphical information from several images with various focus points into a single image with no information loss. We provide a robust image fusion method that can combine two or more degraded input photos into a single clear resulting output image with additional detailed information about the fused input images. The targeted item from each of the input photographs is combined to create a secondary image output. The action level quantities and the fusion rule are two key components of picture fusion, as is widely acknowledged. The activity level values are essentially implemented in either the "spatial domain" or the "transform domain" in most common fusion methods, such as wavelet. The brightness information computed from various source photos is compared to the laws developed to produce brightness / focus maps by using local filters to extract high-frequency characteristics. As a result, the focus map provides integrated clarity information, which is useful for a variety of Multi focus picture fusion problems. Image fusion with several modalities, for example. Completing these two jobs, on the other hand. As a consequence, we offer a strategy for achieving good fusion performance in this study paper. A Convolutional Neural Network (CNN) was trained on both high-quality and blurred picture patches to represent the mapping. The main advantage of this idea is that it can create a CNN model that can provide both the Activity level Measurement" and the Fusion rule, overcoming the limitations of previous fusion procedures. Multi focus image fusion is demonstrated using microscopic images, medical imaging, computer visualization, and Image information improvement is also a benefit of multi-focus image fusion. Greater precision is necessary in terms of target detection and identification. Face recognition" and a more compact work load, as well as enhanced system consistency, are among the new features

Spatial Stimuli Gradient Based Multifocus Image Fusion Using Multiple Sized Kernels

Multi-focus image fusion technique extracts the focused areas from all the source images and combines them into a new image which contains all focused objects. This paper proposes a spatial domain fusion scheme for multi-focus images by using multiple size kernels. Firstly, source images are pre-processed with a contrast enhancement step and then the soft and hard decision maps are generated by employing a sliding window technique using multiple sized kernels on the gradient images. Hard decision map selects the accurate focus information from the source images, whereas, the soft decision map selects the basic focus information and contains minimum falsely detected focused/unfocused regions. These decision maps are further processed to compute the final focus map. Gradient images are constructed through state-ofthe-art edge detection technique, spatial stimuli gradient sketch model, which computes the local stimuli from perceived brightness and hence enhances the essential structural and edge information. Detailed experiment results demonstrate that the proposed multi-focus image fusion algorithm performs better than the other well known state-of-the-art multifocus image fusion methods, in terms of subjective visual perception and objective quality evaluation metrics

Structural similarity loss for learning to fuse multi-focus images

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Convolutional neural networks have recently been used for multi-focus image fusion. However, some existing methods have resorted to adding Gaussian blur to focused images, to simulate defocus, thereby generating data (with ground-truth) for supervised learning. Moreover, they classify pixels as ‘focused’ or ‘defocused’, and use the classified results to construct the fusion weight maps. This then necessitates a series of post-processing steps. In this paper, we present an end-to-end learning approach for directly predicting the fully focused output image from multi-focus input image pairs. The suggested approach uses a CNN architecture trained to perform fusion, without the need for ground truth fused images. The CNN exploits the image structural similarity (SSIM) to calculate the loss, a metric that is widely accepted for fused image quality evaluation. What is more, we also use the standard deviation of a local window of the image to automatically estimate the importance of the source images in the final fused image when designing the loss function. Our network can accept images of variable sizes and hence, we are able to utilize real benchmark datasets, instead of simulated ones, to train our network. The model is a feed-forward, fully convolutional neural network that can process images of variable sizes during test time. Extensive evaluation on benchmark datasets show that our method outperforms, or is comparable with, existing state-of-the-art techniques on both objective and subjective benchmarks

The normalized random map of gradient for generating multifocus image fusion

The multifocus image fusion is a kind of method in image processing to collecting the sharp information from multifocus image sequence. This method is purposed to simplify the reader understand the complex information of image sequence in an image only. There are many methods to generate fused image from several images so far. Many researchers have developed many new and sophisticated methods. They show complicated computation and algorithm. So, that it is difficult to understand by the new students or viewer. Furthermore, they get difficulties to create the new one. In order to handle this problem, the proposed method a concise algorithm which is able to generate an accurate fused image without using a complicated mathematical equation and tough algorithm. The proposed method is the normalized random map of gradient for generating multifocus image fusion. By generate random map of gradient, the algorithm is able to specify the coarse focus region accurately. The random map of gradient is a kind of information formed independently from independent matrix. This data has a significant role in predict the initial focus regions. The proposed algorithm successes to supersede difficulties of mathematical equations and algorithms. It successes to eliminate the mathematical and algorithm problems. Furthermore, the evaluation of proposed method based on the fused image quality. The Mutual Information and Structure Similarity Indexes become our key parameter assessment. The results show that the outputs have high indexes. It means it is acceptable. Then the implementation of multifocus image fusion will increase the quality of the applied fields such as remote sensing, robotics, medical diagnostics and so on. It is also possible implemented in other new fields

Multi-focus image fusion using Content Adaptive Blurring

Multi-focus image fusion has emerged as an important research area in information fusion. It aims at increasing the depth-of-field by extracting focused regions from multiple partially focused images, and merging them together to produce a composite image in which all objects are in focus. In this paper, a novel multi-focus image fusion algorithm is presented in which the task of detecting the focused regions is achieved using a Content Adaptive Blurring (CAB) algorithm. The proposed algorithm induces non-uniform blur in a multi-focus image depending on its underlying content. In particular, it analyzes the local image quality in a neighborhood and determines if the blur should be induced or not without losing image quality. In CAB, pixels belonging to the blur regions receive little or no blur at all, whereas the focused regions receive significant blur. Absolute difference of the original image and the CAB-blurred image yields initial segmentation map, which is further refined using morphological operators and graph-cut techniques to improve the segmentation accuracy. Quantitative and qualitative evaluations and comparisons with current state-of-the-art on two publicly available datasets demonstrate the strength of the proposed algorithm. - 2018 Elsevier B.V.This work was made possible by NPRP grant number NPRP 7-1711-1-312 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu