scholarly journals Deep Color Transfer for Color-Plus-Mono Dual Cameras

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2743
Author(s):  
Hae Woong Jang ◽  
Yong Ju Jung
Keyword(s):  
Image Fusion ◽  
Stereo Matching ◽  
Color Image ◽  
Structural Information ◽  
Just Noticeable Difference ◽  
Color Transfer ◽  
Fused Image ◽  
Visual Artifacts ◽  
Transfer Method ◽  
Deep Learning Model

A few approaches have studied image fusion using color-plus-mono dual cameras to improve the image quality in low-light shooting. Among them, the color transfer approach, which transfers the color information of a color image to a mono image, is considered to be promising for obtaining improved images with less noise and more detail. However, the color transfer algorithms rely heavily on appropriate color hints from a given color image. Unreliable color hints caused by errors in stereo matching of a color-plus-mono image pair can generate various visual artifacts in the final fused image. This study proposes a novel color transfer method that seeks reliable color hints from a color image and colorizes a corresponding mono image with reliable color hints that are based on a deep learning model. Specifically, a color-hint-based mask generation algorithm is developed to obtain reliable color hints. It removes unreliable color pixels using a reliability map computed by the binocular just-noticeable-difference model. In addition, a deep colorization network that utilizes structural information is proposed for solving the color bleeding artifact problem. The experimental results demonstrate that the proposed method provides better results than the existing image fusion algorithms for dual cameras.

Fused Image Quality Measure Based on Structural Similarity

2011 ◽  
Vol 255-260 ◽  
pp. 2072-2076
Author(s):  
Yi Yong Han ◽  
Jun Ju Zhang ◽  
Ben Kang Chang ◽  
Yi Hui Yuan ◽  
Hui Xu
Keyword(s):  
Image Fusion ◽  
Structural Information ◽  
Similarity Index ◽  
Structural Similarity ◽  
Reference Image ◽  
Human Visual Perception ◽  
Subjective Evaluations ◽  
Fused Image ◽  
Fusion Methods ◽  
Image Quality Measure

Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we present a new approach using structural similarity index for assessing quality in image fusion. The advantages of our measures are that they do not require a reference image and can be easily computed. Numerous simulations demonstrate that our measures are conform to subjective evaluations and can be able to assess different image fusion methods.

An Image Fusion Algorithm Based on Pseudo-Color

2012 ◽  
Vol 433-440 ◽  
pp. 5436-5442
Author(s):  
Lei Li
Keyword(s):  
Image Fusion ◽  
Color Image ◽  
Target Identification ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Color Processing ◽  
False Color ◽  
Fused Image ◽  
Color Fusion ◽  
Pseudo Color

The pseudo-color processing for target identification and tracking is very meaningful Experimental results show that the pseudo-color image fusion is a very effective methods. This paper presents a false color image fusion based on the new method. Fusion using wavelet transform grayscale images, find the gray fused image and the difference between the original image, respectively, as the image of l, α, β components are color fusion image, and then after the color transformation, the final false color fused image. The results showed that the color fusion image colors more vivid, more in line with human visual characteristics.

Infrared and Low-Light-Level Image Fusion Method Based on Sparse Representation and Color Transfer

2014 ◽  
Vol 511-512 ◽  
pp. 462-466
Author(s):  
Shi Hong Xu ◽  
Guo Qing Huang ◽  
Cun Chao Liu ◽  
Chun Ping Xiong
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Color Image ◽  
Light Level ◽  
Color Contrast ◽  
Fusion Method ◽  
Color Transfer ◽  
Low Light ◽  
Common Component ◽  
Natural Color

A natural color fusion method for infrared and low-light-level image is proposed. This method utilizes image fusion and color transfer. The fused image uses sparse representation to merge the source images information to be assigned to the Y channel. And then the I and Q channel is combined using Toets method, which extracts the common component from the source images. Finally, the false-color image is obtained by using color transfer technology to the prior pseudo-color YIQ image. Experiments show that the result of our method is information that is more salient, has a higher color contrast, and a more natural color appearance when compared with those produced by the traditional coloration algorithm.

scholarly journals International Journal of Applied Mathematics and Machine Learning

2021 ◽  
Vol 15 (1) ◽  
pp. 1-29
Author(s):  
Xiuming Sun ◽  
◽  
Weina Wu ◽  
Peng Geng ◽  
Lin Lu ◽  
...  
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Color Image ◽  
Applied Mathematics ◽  
Objective Evaluation ◽  
Relevant Information ◽  
Computational Mathematics ◽  
Fused Image ◽  
Focus Image ◽  
Sparse Coefficient

In order to achieve the multi-focus image fusion task, a sparse representation method based on quaternion for multi-focus image fusion is proposed in this paper. Firstly, the RGB color information of each pixel in the color image is represented by quaternion based on the relevant knowledge of computational mathematics, and the color image pixel is processed as a whole vector to maintain the relevant information between the three color channels. Secondly, the dictionary represented by quaternion and the sparse coefficient represented by quaternion are obtained by using the our proposed sparse representation model. Thirdly, the coefficient fusion is carried out by using the “max-L1” rule. Finally, the fused sparse coefficient and dictionary are used for image reconstruction to obtain the quaternion fused image, which is then converted into RGB color multi-focus fused image. Our method belongs to computational mathematics, and uses the relevant knowledge in the field of computational mathematics to help us carry out the experiment. The experimental results show that the method has achieved good results in visual quality and objective evaluation.

A Metaheuristics Framework for Weighted Multi-band Image Fusion

2021 ◽  
pp. 2250016
Author(s):  
Shaheera Rashwan ◽  
Walaa Sheta
Keyword(s):  
Image Quality ◽  
Image Fusion ◽  
Goodness Of Fit ◽  
Color Image ◽  
Landsat 8 ◽  
Public Data ◽  
Fused Image ◽  
Nature Inspired Algorithms ◽  
Band Image ◽  
Multi Band

The main objective of hyper/multispectral image fusion is producing a composite color image that allows for an appropriate visualization of the relevant spatial and spectral information. In this paper, we propose a general framework for spectral weighting-based image fusion. The proposed methodology relies on weight updates conducted using nature-inspired algorithms and a goodness-of-fit criterion defined as the average root mean square error. Simulations on four public data sets and a recent Landsat 8 image of Brullus Lake, Egypt, as an area of study prove the efficiency of the proposed framework. The purpose of the study is to present a framework of multi-band image fusion that produces a fused image of high quality to be further used in computer processing and the results show that the image produced by the presented framework has the highest quality compared with some of the state-of-the art algorithms. To prove the increase in the image quality, we used general quality metrics such as Universal Image Quality Index, Mutual Information, the Variance and Information Measure.

A false color image fusion method based on multi-resolution color transfer in normalization YCC space

Optik ◽  
2014 ◽  
Vol 125 (20) ◽  
pp. 6010-6016 ◽  
Author(s):  
Xuelian Yu ◽  
Jianle Ren ◽  
Qian Chen ◽  
Xiubao Sui
Keyword(s):  
Image Fusion ◽  
Color Image ◽  
Fusion Method ◽  
Color Transfer ◽  
False Color ◽  
False Color Image ◽  
Image Fusion Method

scholarly journals Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 863
Author(s):  
Vidas Raudonis ◽  
Agne Paulauskaite-Taraseviciene ◽  
Kristina Sutiene
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Early Stage ◽  
Image Data ◽  
Cell Detection ◽  
Processing Times ◽  
Fused Image ◽  
Stage Embryo ◽  
Early Stage Embryo

Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.

NSCT and focus measure optimization based multi-focus image fusion

2021 ◽  
pp. 1-13
Author(s):  
N. Aishwarya ◽  
C. BennilaThangammal ◽  
N.G. Praveena
Keyword(s):  
Simulation Analysis ◽  
Structural Information ◽  
Research Area ◽  
Processing Technique ◽  
The Novel ◽  
Phase Congruency ◽  
Focus Measure ◽  
Fused Image ◽  
Focus Image ◽  
Image Pairs

Getting a complete description of scene with all the relevant objects in focus is a hot research area in surveillance, medicine and machine vision applications. In this work, transform based fusion method called as NSCT-FMO, is introduced to integrate the image pairs having different focus features. The NSCT-FMO approach basically contains four steps. Initially, the NSCT is applied on the input images to acquire the approximation and detailed structural information. Then, the approximation sub band coefficients are merged by employing the novel Focus Measure Optimization (FMO) approach. Next, the detailed sub-images are combined using Phase Congruency (PC). Finally, an inverse NSCT operation is conducted on synthesized sub images to obtain the initial synthesized image. To optimize the initial fused image, an initial decision map is first constructed and morphological post-processing technique is applied to get the final map. With the help of resultant map, the final synthesized output is produced by the selection of focused pixels from input images. Simulation analysis show that the NSCT-FMO approach achieves fair results as compared to traditional MST based methods both in qualitative and quantitative assessments.

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