A New Robust Multi focus image fusion Method Khubaib Iqbal 1 , Tehreem Masood 2 , Arfan Jaffar 3 , Hafiz Muhammad Tayyab Khushi 4 1, 4 Department of Computer Science, Superior University Lahore Email: muhammad.tayyab.khushi@superior.edu.pk ABSTRACT 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. KEYWORDS: Image fusion, Multi focus image fusion, Blurring, blurred images, spatial, Convolutional NeuralNetwork, Activity level measurements 1. INTRODUCTION The background of multi focus picture fusion in terms of scientific research is presented in this chapter. We also go through the thesis's goals, as well as our major contribution structure and the working atmosphere. Picture processing is a method of improving or extracting information from an image by performing operations on it. It is a type of signal processing where the image acts as the input and the output is that image or the features associated with it. Image processing is a method of refining or extracting information by working on an image. It is a type of signal processing where the image acts as the inputand the output is that image or the features associated with it. The image fusion process is defined as collecting all relevant data from numerous pictures and combining it into a smaller number of images, generally only one. This one image has all of the required information and is more useful and precise than any single source image. The goal of picturefusion is to create images that are more suitable and comprehensible for human and machine perception, not just to minimize the amount of data. Multisensory image fusion is a computer vision technique that combines important data or information from two or more images into a single image.The increasing availability of space-borne sensors in distant sensing applications provides impetus for various picture fusion techniques. Several image processing scenarios need great spatial and spectral resolution in a single picture. The majority of current technology is incapable of delivering such information credibly. Image fusion methods allow diverse information sources to be combined. The spatial and spectral resolution properties of the merged picture might be complimentary. The processof multi focus image is defined as collecting all the significant information from any input images and make single image. Single image is much useful and precise than any input image it contains all compulsory info. Main aim of the image fusion isn't simply shrink total quantity of data it also use tobuild images that provides more informative results than input images.