High dynamic range compression and detail enhancement of infrared images in the gradient domain

Zhang, Feifei; Xie, Wei; Ma, Guorui; Qin, Qianqing

doi:10.1016/j.infrared.2014.09.003

Cited by 36 publications

(15 citation statements)

References 29 publications

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“…The brief characteristics of the test images are described in Table 1; the original test images were blurry due to the atmospheric turbulence and point spread function (PSF) of the optical lens. Because there is no universal objective criterion for IR image quality assessment, and several blind image quality assessment metrics perform inconsistently on IR images [3], we performed a comparison using the average gradient (AG), which is widely used as an indicator for the evaluation of the edge detail contrast and sharpness characteristics of an image [4]. The AG can be calculated as follows:…”

Section: Experimental Results Comparison and Discussionmentioning

confidence: 99%

“…The dominant IR image detail improvement methods can be divided into mapping-based approaches, gradient-domain algorithms, and decomposition-based methods [3]. The typical disadvantages of gradient-domain algorithms [4] typically include being computationally expensive and having poor stability, while the mapping-based approaches [5,6] provide relatively limited improvements in the details. Consequently, the existing high-performance detail enhancement methods for IR image processing are decomposition-based methods [7], which separate a detail layer from the input image and assign higher weight before merging, providing outstanding detail and contrast enhancement performance.…”

Section: Introductionmentioning

confidence: 99%

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High-Performance Enhancement of SWIR Images

Zhi

et al. 2022

Electronics

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Short-wave infrared imaging technology has rapidly developed over the past decade. However, image detail and contrast enhancement techniques, which are crucial for infrared imaging systems, are rarely dedicated to SWIR imaging systems. Moreover, the existing IR image enhancement methods generally have difficulty providing real-time performance, which plays a significant role in imaging systems with high data rates. In this paper, we propose a simple and real-time SWIR image enhancement approach based on the difference of Gaussian filter and histogram remapping, and illustrate the implementation of the proposed method on FPGA. The experimental results demonstrated that our method achieves promising detail and contrast enhancement performance with a frame rate of around 50 fps for high-definition images, and that extremely high frame rates can be achieved with pipelined architecture.

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Section: Experimental Results Comparison and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-Performance Enhancement of SWIR Images

Zhi

et al. 2022

Electronics

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show abstract

“…Considering the relatively low signal-to-noise ratio (SNR) characteristics of IR images, some researchers adopted wavelet related algorithms to achieve better noise-reducing performance and edges preserving effects [11,12], while others separated the detail/edge information from the original IR image for different downstream tasks [13][14][15][16][17]. Furthermore, top-hat transform [18], gradient domain [19,20], shearlet domain [21,22] and frequency domain [23] have also been investigated for the IR edge/detail enhancement purpose. Some other related works including an improved unsharp mask algorithm [24], gradient distribution via Cellular Automata [25], morphological operators [26], all-optical upconversion imaging techniques [27], the iterative contrast enhancement method [28], and the gravitational force and lateral inhibition network [29].…”

Section: Non-deep Learning Based Approachesmentioning

confidence: 99%

Infer Thermal Information from Visual Information: A Cross Imaging Modality Edge Learning (CIMEL) Framework

Wang

Mei

Yang

et al. 2021

Sensors

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The measurement accuracy and reliability of thermography is largely limited by a relatively low spatial-resolution of infrared (IR) cameras in comparison to digital cameras. Using a high-end IR camera to achieve high spatial-resolution can be costly or sometimes infeasible due to the high sample rate required. Therefore, there is a strong demand to improve the quality of IR images, particularly on edges, without upgrading the hardware in the context of surveillance and industrial inspection systems. This paper proposes a novel Conditional Generative Adversarial Networks (CGAN)-based framework to enhance IR edges by learning high-frequency features from corresponding visual images. A dual-discriminator, focusing on edge and content/background, is introduced to guide the cross imaging modality learning procedure of the U-Net generator in high and low frequencies respectively. Results demonstrate that the proposed framework can effectively enhance barely visible edges in IR images without introducing artefacts, meanwhile the content information is well preserved. Different from most similar studies, this method only requires IR images for testing, which will increase the applicability of some scenarios where only one imaging modality is available, such as active thermography.

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“…Another approach is to express the DRR problem as the minimization of a function in the gradient domain. 23 Qiao and Ng 24 presented a method to reduce the dynamic range of high-dynamic-range visible image by using localized gamma correction. The idea was adopted from a paper focused on IR image data, but applied to the visible domain.…”

Section: Infrared Imagingmentioning

confidence: 99%

No-reference prediction of quality metrics for H.264-compressed infrared sequences for unmanned aerial vehicle applications

2019

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We propose a no-reference (NR) method for estimating the scores of metrics assessing the quality of infrared (IR) sequences compressed with H.264 for low-complexity unmanned aerial vehicle (UAV) applications. The scenario studied is to estimate the quality on an on-ground computer to avoid performing the processing onboard, due to the computational and memory limitations of the onboard hardware. For low complexity and fast feedback, a bitstream-based (BB) approach was chosen. The original IR sequences are captured by UAV, and then BB and pixel-based (PB) features are computed. Thereafter, a feature selection process is applied and the selected features are mapped using support vector regression, to predict the quality scores of full reference metrics. The method is evaluated for the NR prediction of four image and one video quality metrics. A set of five UAV-and three ground-IR sequences are used for evaluation. The proposed NR method consistently achieves robust results for the different objective metrics tested (Spearman rank-order correlation coefficients ranging from 0.91 to 0.99). A comparison with estimations based on features from three NR models from the literature proves to be in favor of the proposed method.

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High dynamic range compression and detail enhancement of infrared images in the gradient domain

Cited by 36 publications

References 29 publications

High-Performance Enhancement of SWIR Images

High-Performance Enhancement of SWIR Images

Infer Thermal Information from Visual Information: A Cross Imaging Modality Edge Learning (CIMEL) Framework

No-reference prediction of quality metrics for H.264-compressed infrared sequences for unmanned aerial vehicle applications

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