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Figure 7 shows examples of IF in the photography domain, the fusion of multi-focus images. It is not possible for all objects with diverse distances from camera due to its restricted depths to be all-in-focus within a single shot of cameras. Due to the restricted depths of the camera, it is not possible to be all-in-focus within a single shot of cameras for all objects with diverse distances. To overcome this, the multi-focus IF method is used to merge several images with a similar scene having diverse focus points for generating an all-in-focusresultant image. This resultant compound image can well defend the significant Information from the source image. It is more desirable in several image processing tasks and machine vision. In Fig. 8, the data sources used in the photography domain. The various challenges which are faced in this domain are:
Despite the various constraints which are handled by several researchers, still number of research and development in the field of image fusion is growing day by day. Image fusion has several open-ended difficulties in different domains. The main aim is to discuss the current challenges and future trends of image fusion that arise in various domains, such as surveillance, photography, medical diagnosis, and remote sensing are analyzed in the fusion processes. This paper has discussed various spatial and frequency domain methods as well as their performance evaluation measures. Simple image fusion techniques cannot be used in actual applications. PCA, hue intensity saturation and Brovey methods are computationally proficient, high-speed and extremely straightforward but resulted in distortion of color. Images fused with Principal component analysis have a spatial advantage but resulted in spectral degradation. The guided filtering is an easy, computationally efficient method and is more suitable forreal-world applications. The number of decomposition levels affects the pyramid decomposition in image fusion outcome. Every algorithm has its own advantages and disadvantages. The main challenge faced in remote sensing field is to reduce the visual distortions after fusing panchromatic (PAN), hyperspectral (HS) and multi-spectral (MS) images. This is because source images are captured using different sensors with similar platform but do not focus on a same direction as well as their gaining moments are not exactly the same. The dataset and its accessibility represent a restriction that is faced by many researchers. The progress of image fusion has increased its interest in colored images and its enhancement. The aim of color contrast enhancement is to produce an appealing image with bright color and clarity of the visual scene. Recently, researchers have used neutrosophy in image fusion, used to remove noise and to enhance the quality of single photon emission tomography (SPET), computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) image. This integration of neutrosophy with image fusion resulted in noise reduction and better visibility of the fused image. Deep learning is the rising trend to develop the automated application. It extremely applied in various applications such as face recognition, speech recognition, object detection and medical imaging. The integration of quantitative and qualitative measures is the accurate way to determine which particular fusion technique is better for certain application. The various challenges which generally are faced by researchers is to design image transformation and fusion strategies. Moreover, the lack of effective image representation approaches and widely recognized fusion evaluation metrics for performance evaluation of image fusion techniques is also of great concern. Whereas the recent progresses in machine learning and deep learning based image fusion shows a huge potential for future improvement in image fusion.
Recently, the area of image fusion is attracting more attention. In this paper, various image fusion techniques with their pros and cons, different methods with state-of-art has been discussed. Different applications like medical image, remote sensing, photography and surveillance images have been discussed with their challenges. Finally, the different evaluation metrics for image fusion techniques with or without reference has been discussed. Therefore, it is concluded from survey that each image fusion technique is meant for a specific application and can be used in various combinations to obtain better results. In future, new deep neural networks based image fusion methods will be developed for various domains to improve the efficiency of fusion procedure by implementing the algorithm with parallel computing unit.
The goal of this work is to provide an empirical basis forresearch on image segmentation and boundary detection. To this end, wehave collected 12,000 hand-labeled segmentations of 1,000 Corel dataset images from30 human subjects. Half of the segmentations were obtained from presenting thesubject with a color image; the other half from presenting a grayscaleimage. The public benchmark based on this data consists of all of the grayscaleand color segmentations for 300 images. The images are divided into a training setof 200 images, and a test set of 100 images.We have also generated figure-ground labelings for a subset of these images whichmay be found hereWe have used this data for bothdeveloping new boundary detection algorithms, and for developing a benchmark forthat task. You may download a MATLAB implementation of our boundarydetector below, along with code for running the benchmark. We arecommitted to maintaining a public repository of benchmark results in the spiritof cooperative scientific progress. On-Line Browsing DatasetBy Image -- This page contains the list of all the images. Clicking on an image leads youto a page showing all the segmentations of that image.By Human Subject -- Clicking on a subject's ID leads you to a page showing all of the segmentations performed by that subject.Benchmark Results By Algorithm -- This page shows the list of tested algorithms, ordered as they perform on the benchmark. By Image -- This page shows the test images. The images are ordered by how well any algorithm can find boundaries, so that it is easy to see which images are "easy" and which are "hard" for the machine.On all of these pages, there are many cross-links between images, subjects,and algorithms. Note that many of the smaller images are linked tofull-size versions.
The following files may be of particular interest:VERSIONCHANGELOGREADMEBenchmark/README Submitting Benchmark Results If you have a boundary detector or segmentation algorithm, your results on the test images should be put in the form of 8-bitgrayscale BMP images. These images should be the same size asthe benchmark images (481x321 pixels), and should be named .bmp, where is the image IDnumber. You should also create a name.txt file containinga 1-line text descriptor for your algorithm, and an optionalabout.html file with a short description. Thedescription can contain html links. In the downloads section above, you will find the code for running thebenchmark, as well as scripts for generating web pages. This code isknown to build and work on Intel/Linux platforms. We do not supportWindows, although we know of at least one case where the code wasbuild successfully on Windows using Cygwin.The code has also been built succesfully on Mac Intel (see notes here). You will need Matlabto run the benchmark. If you have the appropriate hardware and software,then please download the code and run the benchmark yourself. To submitresults, tar up your algorithm directory and send us a URL fromwhich we can download it.If you are unable to run the benchmark yourself, then you maysubmit a tarball containing your algorithm's results with thename.txt and about.html files. We will run the benchmark for you, butwe cannot guarantee quick turnaround. Segmentation results should be in the form of binary images where a"1" marks the segment boundary pixels. Boundarydetection results can also be in this form, but we strongly encouragea "soft" boundary representation. Submitting a softoutput will remove the burden on you of choosing an optimal threshold,since the benchmark will find this threshold for you. Note alsothat for best results, the boundaries should be thinned, e.g.by performing non-maxima supression. The benchmark will handlethick boundaries, but the morphological thinning operation that wedo to thin boundaries may not be optimal for your algorithm.Please Note: Although this should go without saying, wewill say it anyway. To ensure the integrity of results on thetest data set, you may use the images and human segmentations in thetraining set for tuning your algorithms but your algorithms should nothave access to any of the data (images or segmentations) in the testset until your are finished designing and tuning youralgorithm. About the Benchmark "When you can measure what you are speaking about and express it in numbers,you know something about it; but when you cannot measure it, when you cannotexpress it in numbers, your knowledge is of the meager and unsatisfactorykind." --Lord KelvinThe goal of the benchmark is to produce a score for an algorithm's boundariesfor two reasons: (1) So that different algorithms can be compared to eachother, and (2) So that progress toward human-level performance can be trackedover time. We have spent a great deal of time working on a meaningfulboundary detection benchmark, which we will explain briefly here. See our NIPS2002and PAMIpapers for additional details. Note that the methodology we have settledon may be applied to any boundary dataset -- not just our dataset of humansegmented natural images. 2b1af7f3a8