Multispectral image enhancement based on irradiation-reflection model and bounded operation

Guo-Ling, Bi; Xu, Zhijun; Jian, Zhao; Qiang, Sun

doi:10.7498/aps.64.100701

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…Indeed, edge extraction technology [1][2][3][4] has always been an important research topic in image processing. It has applications such as image enhancement [5], image restoration [6], and image segmentation [7]. Various edge extraction operators in digital image processing [8] have been used to obtain edge information.…”

Section: Introductionmentioning

confidence: 99%

Isotropic Two-Dimensional Differentiation Based on Dual Dynamic Volume Holograms

Fan

Zhang

et al. 2023

Photonics

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We study the use of two dynamic thick holograms to realize isotropic two-dimensional (2D) differentiation under Bragg diffraction. Acousto-optic modulators (AOMs) are used as dynamic volume holograms. Using a single volume hologram, we can accomplish a first-order derivative operation, corresponding to selective edge extraction of an image. Since the AOM is a 1D spatial light modulator, filtering of the image only occurs along the direction of the sound propagation. To achieve 2D image processing, two AOMs are used within a Mach–Zehnder interferometer (MZI). By aligning one AOM along the x-direction on the upper arm of the interferometer and another AOM along the y-direction on the lower arm, we accomplish the sum of two first-derivative operations, leading to isotropic edge extraction. We have performed both computer simulations and optical experiments to verify the proposed idea. The system provides additional operations in optical computing using AOMs as dynamic holograms.

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Section: Introductionmentioning

confidence: 99%

Isotropic Two-Dimensional Differentiation Based on Dual Dynamic Volume Holograms

Fan

Zhang

et al. 2023

Photonics

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A Spatial-Frequency Domain Associated Image-Optimization Method for Illumination-Robust Image Matching

Liu

Jia

et al. 2020

Sensors

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Image matching forms an essential means of data association for computer vision, photogrammetry and remote sensing. The quality of image matching is heavily dependent on image details and naturalness. However, complex illuminations, denoting extreme and changing illuminations, are inevitable in real scenarios, and seriously deteriorate image matching performance due to their significant influence on the image naturalness and details. In this paper, a spatial-frequency domain associated image-optimization method, comprising two main models, is specially designed for improving image matching with complex illuminations. First, an adaptive luminance equalization is implemented in the spatial domain to reduce radiometric variations, instead of removing all illumination components. Second, a frequency domain analysis-based feature-enhancement model is proposed to enhance image features while preserving image naturalness and restraining over-enhancement. The proposed method associates the advantages of the spatial and frequency domain analyses to complete illumination equalization, feature enhancement and naturalness preservation, and thus acquiring the optimized images that are robust to the complex illuminations. More importantly, our method is generic and can be embedded in most image-matching schemes to improve image matching. The proposed method was evaluated on two different datasets and compared with four other state-of-the-art methods. The experimental results indicate that the proposed method outperforms other methods under complex illuminations, in both matching performances and practical applications such as structure from motion and multi-view stereo.

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Multispectral image enhancement based on Retinex by using structure extraction

Li¹,

Wu²,

Yang³

et al. 2016

Acta Phys. Sin.

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Applications in industrial and scientific areas need high-quality multispectral images. However, because of various disadvantages, e.g., adverse environment of sensing, limited spectral bands, etc., multispectral images suffer low contrasts, low signal-noise-ratios, etc. As inputs of applications such as tracking, recognition and so on, multispectral images with low quality may cause those applications to fail. In this paper, aiming to meet practical requirements, we propose an algorithm of efficiently improving miultispectral images. The framework of the proposed method is as follows. According to Retinex theory, a multispectral image can be modeled as multiplicative combination of an illumination component and a reflection component. Typically, an illumination component is of low frequency and determines the dynamic range of pixel intensity, while a reflection component is of high frequency and determines the property of an image. Once we can successfully enhance both an illumination component and a reflection component, which is described later, respectively, we achieve the enhancement of a multispectral image by multiplying two enhanced components. First, we estimate the illumination component based on the principal structures extracted from the multispectral image on a multiple scale, i.e. on a low-level scale, middle-level scale and high-level scale, respectively. The mean value of the three principal structures is used as the estimation of an illumination component. Then the global structure contained in the illumination component, can be obtained by analyzing the corresponding information about its histogram, and is involved to enhance the global contrast and the edge details of the principal structure. Second, with the previously computed illumination component, we can easily derive the reflection component from the multispectral image in pixel-wise division operation. There are adequate image details as well as noise in the reflection component. We suppress the noise and keep the image details by using a non local mean filter. And then we enhance the image details by means of local variances. Finally, multiplying the enhanced illumination component by the filtered reflection component, we enhance the multispectral image. In order to verify the efficiency of our algorithm, experiments are conducted over multispectral image sets including X-ray images, ultraviolet images, well illuminated visible light images, poorly illuminated visible light image, and infrared images. The experimental results show that the proposed algorithm can efficiently remove halo artifacts, well suppress noise and obviously improve local details as well as global contrast. Compared with the state-of-the-art algorithms, the proposed method significantly enhances multispectral images both in vision and in objective analysis by means of information entropy and average gradient.

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Multispectral image enhancement based on irradiation-reflection model and bounded operation

Cited by 6 publications

References 12 publications

Isotropic Two-Dimensional Differentiation Based on Dual Dynamic Volume Holograms

Isotropic Two-Dimensional Differentiation Based on Dual Dynamic Volume Holograms

A Spatial-Frequency Domain Associated Image-Optimization Method for Illumination-Robust Image Matching

Multispectral image enhancement based on Retinex by using structure extraction

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