Digital Image Fusion Using HVS in Block Based Transforms

Radhika, V.; Veeraswamy, K.; Kumar, S. Srinivas

doi:10.1007/s11265-017-1252-8

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…It is widely used in image feature extraction, classifcation, and information monitoring, so it is deeply concerned by relevant scholars. In general, according to the types of source images involved in the fusion, the progressive image fusion can be roughly divided into the fusion of infrared image and visible image, the fusion of multispectral image and panchromatic image, the fusion of multifocus image, and the fusion of medical image [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Digital Image Progressive Fusion Method Based on Discrete Cosine Transform

Chen

2023

Journal of Mathematics

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The current progressive fusion methods for digital images have poor denoising performance, which leads to a decrease in image quality after progressive fusion. Therefore, a new method for digital image progressive fusion was proposed based on discrete cosine transform, and its effectiveness was verified through experiments. The experimental results show that the proposed method has a PSNR value higher than 42.13 db in image fusion, both of which are higher than the comparison method, and the fusion effect comparison also has higher image quality. In terms of fusion time, the time of the research method is lower than that of the comparison method when the data volume is between 10 and 100, while in the comparison of structural similarity, the structural similarity of the image fused by the research method is always higher than 0.83. Overall, the fusion method proposed in the study results in higher image quality and is effective in progressive digital image fusion, which is of great significance for practical digital image fusion.

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Section: Introductionmentioning

confidence: 99%

Digital Image Progressive Fusion Method Based on Discrete Cosine Transform

Chen

2023

Journal of Mathematics

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“…Fusion for digital images [1] is the practice of associating correspondent details from several images into an image. Image fusion processes are extensively employed by current applications such as bio-medical technology, satellite map, and remote sensors.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of weighted fusion for scalar images in multi-sensor network

Jittawiriyanukoon¹,

Srisarkun²

2021

Bulletin EEI

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The regular image fusion method based on scalar has the problem how to prioritize and proportionally enrich image details in multi-sensor network. Based on multiple sensors to fuse and manipulate patterns of computer vision is practical. A fusion (integration) rule, bit-depth conversion, and truncation (due to conflict of size) on the image information are studied. Through multi-sensor images, the fusion rule based on weighted priority is employed to restructure prescriptive details of a fused image. Investigational results confirm that the associated details between multiple images are possibly fused, the prescription is executed and finally, features are improved. Visualization for both spatial and frequency domains to support the image analysis is also presented.

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“…The Human Visual System (HVS) is able to recognize objects easily in a cluttered scene in less than a second. Recent works inspiring the Human Visual System (HVS) in image processing are such as image enhancement [1], data hiding [2,3], digital image fusion [4], robust object recognition [5]. HVS processes images easily, while the most powerful computer systems are generally not capable of doing so.…”

Section: Introductionmentioning

confidence: 99%

Object Recognition Inspiring HVS

Akbarpour

Mehrshad

Razavi

2018

IJEECS

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<p><span>Human recognize objects in complex natural images very fast within a fraction of a second. Many computational object recognition models inspired from this powerful ability of human. The Human Visual System (HVS) recognizes object in several processing layers which we know them as hierarchically model. Due to amazing complexity of HVS and the connections in visual pathway, computational modeling of HVS directly from its physiology is not possible. So it considered as a some blocks and each block modeled separately. One models inspiring of HVS is HMAX which its main problem is selecting patches in random way. As HMAX is a hierarchical model, HMAX can enhanced with enhancing each layer separately. In this paper instead of random patch extraction, Desirable Patches for HMAX (DPHMAX) will extracted. HVS for extracting patch first selected patches with more information. For simulating this block patches with more variance will be selected. Then HVS will chose patches with more similarity in a class. For simulating this block one algorithm is used. For evaluating proposed method, Caltech 5 and Caltech101 datasets are used. Results show that the proposed method (DPMAX) provides a significant performance over HMAX and other models with the same framework.</span></p>

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Digital Image Fusion Using HVS in Block Based Transforms

Cited by 6 publications

References 27 publications

Digital Image Progressive Fusion Method Based on Discrete Cosine Transform

Digital Image Progressive Fusion Method Based on Discrete Cosine Transform

Evaluation of weighted fusion for scalar images in multi-sensor network

Object Recognition Inspiring HVS

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