The necessity of image fusion is growing in recently in image processing applications due to the tremendous amount of acquisition systems. Fusion of images is defined as an alignment of noteworthy Information from diverse sensors using various mathematical models to generate a single compound image. The fusion of images is used for integrating the complementary multi-temporal, multi-view and multi-sensor Information into a single image with improved image quality and by keeping the integrity of important features. It is considered as a vital pre-processing phase for several applications such as robot vision, aerial, satellite imaging, medical imaging, and a robot or vehicle guidance. In this paper, various state-of-art image fusion methods of diverse levels with their pros and cons, various spatial and transform based method with quality metrics and their applications in different domains have been discussed. Finally, this review has concluded various future directions for different applications of image fusion.

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Image Fusion Techniques: A Survey

In remote sensing applications and medical imaging, one of the key points is the acquisition, real-time preprocessing and storage of information. Due to the large amount of information present in the form of images or videos, compression of these data is necessary. Compressed sensing is an efficient technique to meet this challenge. It consists in acquiring a signal, assuming that it can have a sparse representation, by using a minimum number of nonadaptive linear measurements. After this compressed sensing process, a reconstruction of the original signal must be performed at the receiver. Reconstruction techniques are often unable to preserve the texture of the image and tend to smooth out its details. To overcome this problem, we propose, in this work, a compressed sensing reconstruction method that combines the total variation regularization and the non-local self-similarity constraint. The optimization of this method is performed by using an augmented Lagrangian that avoids the difficult problem of nonlinearity and nondifferentiability of the regularization terms. The proposed algorithm, called denoising-compressed sensing by regularization (DCSR) terms, will not only perform image reconstruction but also denoising. To evaluate the performance of the proposed algorithm, we compare its performance with state-of-the-art methods, such as Nesterov’s algorithm, group-based sparse representation and wavelet-based methods, in terms of denoising and preservation of edges, texture and image details, as well as from the point of view of computational complexity. Our approach permits a gain up to 25% in terms of denoising efficiency and visual quality using two metrics: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).

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Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints

Multi-focus Image Fusion using Neutrosophic based Wavelet Transform

Multi-Focus Image Fusion (MFIF) is a method that combines two or more source images to obtain a single image which is focused, has improved quality and more information than the source images Due to limited Depth-of-Field of the imagining system, extracting all the useful information from a single image is challenging Thus two or more defocused source images are fused together to obtain a composite image This paper provides a comprehensive overview of existing MFIF methods A new classification scheme is developed for categorizing the existing MFIF methods These methods are classified into four major categories: spatial domain, transform domain, deep leaning and their hybrids and have been discussed well along with their drawbacks and challenges In addition to this, both the parametric evaluation metrics ie "with reference" and "without reference" have also discussed Then, a comparative analysis for nine image fusion methods is performed based on 30 pairs of publicly available images Finally, various challenges that remain unaddressed and future work is also discussed in this work

Multi-focus image fusion techniques: a survey

To increase reliability, manufacturers must be able to discover a high percentage of defective chips during their testing procedures. This paper would highlight the attention given by most customers who are expecting the designer to include testability features that will increase their product reliability. This paper focuses on the design of a UART chip with embedded built-in-self-test (BIST) architecture using FPGA technology. The paper starts by describing the behavior of UART circuit using VHISC hardware description language (VHDL). In the implementation phase, the BIST technique will be incorporated into the UART design before the overall design is synthesized by means of reconfiguring the existing design to match testability requirements.

A VHDL implementation of BIST technique in UART design / by Mohd. Yamani Idna bin Idris.

UART abbreviated as Universal Asynchronous Receiver Transmitter is one of the essential element of communication system. It is being mostly used when there is a short-distance, between computer and peripherals. Whenever there is low-cost data exchange or the speed required for transmission is not high, UART’s are also being used there. For the achievement of compact, stable and reliable data transmission, the implementation of UART with VHDL language can be integrated into FPGA. The Total power and Junction temperature of UART have been analyzed in the following paper when it is operating on different I/O standards of HSTL (HIGH SPEED TRANSCEIVER LOGIC) logic family and different range of frequencies from 1 GHz to 46 GHz. Analysis have also been done for two different 28nm FPGA’s which helps to compare the total power reduction at two different FPGA technology so that the best suited FPGA for UART design consuming the least could be discovered. After analysis, it has been concluded that 91.96% of the total power can be saved in the case Kintex-7 and 91.98% of total power can be preserved in case of Artix-7 by operating the design at a frequency of 1 GHz. On the other hand the Junction temperature has been reduced to 11.84% in case of Kintex-7 and it has been reduced to 15.28% in case of Artix-7.

28nm FPGA based Power Optimized UART Design using HSTL I/O Standards

With the recent development of web 2.0, there has been a lot of increase in social networking and online marketing sites. The data obtained from these sites are analysed for better human decision making. Sentiment analysis involves extraction of sentiments from reviews. Sentiments can be positive, negative or neutral. Most of the content on the internet is in the English language, but with the improved awareness of people, data in other languages is increasing gradually. Not much work has been done in Indian languages. Hinglish, a mixture of Hindi and English, is an informal language which is exceptionally famous in India as individuals feel greater talking in their language. In this paper, we present a dictionary-based approach for Hinglish text classification. We also implemented traditional machine learning classification algorithms such as SVM, NB and ME for comparison. It is found that for Hinglish text, dictionary-based classification gives best accuracy results.

Dictionary-based sentiment analysis of Hinglish text and comparison with machine learning algorithms

Image fusion acts as a powerful tool in the medical domain. It is an essential method for enhancing the quality of images by combining the complementary images which are captured from various sensors or cameras. The aim of multi modal image fusion technique is to obtain a single fused image by fusing the images of different modalities. It is widely used in various clinical applications for better diagnosis of several types of diseases. In this paper, comparative analysis has been made using various multi modal techniques in medical domain such as guided filter, multi resolution singular value decomposition and principal component analysis. The quantitative and qualitative results have been taken. The experiment results indicated that guided filter method is more efficient as compared to other methods in terms of evaluation metrics such as standard deviation is 29.8, mean is 52.3, entropy is 2.8 and fusion information score is 0.8. It has also been observed that guided filter is able to preserve edges efficiently and is more suitable for real applications.

Harpreet Kaur

Papers

Image Fusion Techniques: A Survey

28nm FPGA based Power Optimized UART Design using HSTL I/O Standards

Dictionary-based sentiment analysis of Hinglish text and comparison with machine learning algorithms

Multi Modal Image Fusion: Comparative Analysis

Electrical characteristics analysis of PIN detector with different doping concentration levels of N and P-type materials