An Enhanced Image Fusion Algorithm by Combined Histogram Equalization and Fast Gray Level Grouping Using Multi-Scale Decomposition and Gray-PCA

Jameel Ahmed Bhutto,Yu Lubin,Muhammad Faizan Tahir,Tian Lianfang,Toufique Ahmed Soomro,Qiliang Du

doi:10.1109/access.2020.3018264

Abstract

Image enhancement is a challenging task in image analysis particularly, it is more challenging in performing image fusion. Image fusion is the process of combining multiple images to produce quality output without any variation in contrast, blurring, and noise. Many image fusion algorithms have been implemented, but their final fused images suffer from variations in background contrast, uneven illumination, blurring, and the presence of noise. To overcome the aforementioned issues, this paper proposed a new image fusion method, which improves image contrast and also gives appropriate details of the image. Our method is based on a set of conventional techniques such as amalgamated histogram equalization and fast gray-scale grouping to handle the problems mentioned, and we improve overall fusion strategies by proposing a novel principal component analysis technique to convert RGB types images to high gray-scale contrast image as the final output image. We have carried out many experiments on different common databases used by various researchers. Our proposed method gives good subjective and objective performances compared to other statuses. Our proposed method can be used in different monitoring applications.

Highlights

The image fusion is the way of merging multiple input images to produce one output image which extracts high quality and more informative for the perception of human vision, robot and other processing tasks as compared to any of the input images [1,2,3]
The main contributions of this work are as follows: i) An amalgamated histogram equalization and fast graylevel grouping (HEFGLG) technique is proposed in this work that automatically enhances the contrast of image
The median-average based discrete stationary wavelet transform (DSWT)-PRINCIPAL COMPONENT ANALYSIS (PCA), non-subsampled shearlet transform (NSST)-spatial frequency (SF)-pulse coupled neural network (PCNN) and morphology-hat transform (MT)-computed tomography (CT)-PCA are new hybrid image fusion algorithms, which perform better than other techniques but the proposed method achieves the best performance among the aforementioned schemes

Summary

INTRODUCTION

The image fusion is the way of merging multiple input images to produce one output image which extracts high quality and more informative for the perception of human vision, robot and other processing tasks as compared to any of the input images [1,2,3]. Pixel-level based fusion is used by many researchers in various applications It merges the pixels of input images directly to acquire the final output image [1, 3]. Some researchers have taken attention on feature-level based image fusion that deals with high-level processing tasks. The main key goal of any image fusion algorithm is to increase contrast of source images so that it can preserve most of the useful information without producing artifacts and apply proper fusion strategy that should be robust to improper conditions. The main contributions of this work are as follows: i) An amalgamated histogram equalization and fast graylevel grouping (HEFGLG) technique is proposed in this work that automatically enhances the contrast of image This method is computationally efficient and it produces better results.

THE RELATED WORK

DATASETS

Findings

CONCLUSION