A multiresolution approach to image enhancement via histogram shaping and adaptive Wiener filtering

Pace, Teresa; Manville, Drew; Lee, H.; Cloud, Gene; Puritz, James

doi:10.1117/12.778038

Cited by 6 publications

(4 citation statements)

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“…Advancements have also been made in infrared image enhancement through multiscale and multiresolution approaches. Pace et al [30] introduced a multiscale pyramid method for image decomposition. To further enhance image enhancement, the Retinex-based image enhancement method [31], which simulates human visual mechanisms, has been introduced to infrared images.…”

Section: Traditional Image Enhancement Methodsmentioning

confidence: 99%

A Dual Convolutional Neural Network with Attention Mechanism for Thermal Infrared Image Enhancement

Gao,

Zhang,

Wang

et al. 2023

Electronics

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In industrial applications, thermal infrared images, which are commonly used, often suffer from issues such as low contrast and blurred details. Traditional image enhancement algorithms are limited in their effectiveness in improving the visual quality of thermal infrared images due to the specific nature of the application. Therefore, we propose a dual Convolutional Neural Network (CNN) combined with an attention mechanism to address the challenges of enhancing low-quality thermal infrared images and improving their visual quality. Firstly, we employ two parallel sub-networks to extract both global and local features. In one sub-network, we utilize a sparse mechanism incorporating dilated convolutions, while the other sub-network employs Feature Attention (FA) blocks based on channel attention and pixel attention. This architecture significantly enhances the feature extraction capability. The use of attention mechanisms allows the network to filter out irrelevant background information, enabling more flexible feature extraction. Finally, through a simple yet effective fusion block, we thoroughly integrate the extracted features to achieve an optimal fusion strategy, ensuring the highest quality enhancement of the final image. Extensive experiments on benchmark datasets and real images demonstrate that our proposed method outperforms other state-of-the-art models in terms of objective evaluation metrics and subjective assessments. The generated images also exhibit superior visual quality.

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Section: Traditional Image Enhancement Methodsmentioning

confidence: 99%

A Dual Convolutional Neural Network with Attention Mechanism for Thermal Infrared Image Enhancement

Gao,

Zhang,

Wang

et al. 2023

Electronics

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“…The size of the tiles would vary depending on the FOV of the sensor and the particular CONOP. An additional approach if the degraded image is too poor is to apply a multi-resolution wavelet transform approach to compress each of the tiles and then transmit only the appropriate wavelet coefficients associated with each tile [3][4][5][6].…”

Section: Proposed Methods Of Image Transmission Reductionmentioning

confidence: 99%

Technologies for low-bandwidth high-latency unmanned ground vehicle control

et al. 2014

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Automation technology has evolved at a rapid pace in recent years; however, many real-world problems require contextual understanding, problem solving, and other forms of higher-order thinking that extends beyond the capabilities of robots for the foreseeable future. This limits the complexity of automation which can be supplied to modern unmanned ground robots (UGV) and necessitates human-in-the-loop monitoring and control for some portions of missions. In order for the human operator to make decisions and provide tasking during key portions of the mission, existing solutions first derive significant information from a potentially dense reconstruction of the scene utilizing LIDAR, video, and other onboard sensors. A dense reconstruction contains too much data for real-time transmission over a modern wireless data link, so the robot electronics must first condense the scene representation prior to transmission. The control station receives this condensed scene representations and provides visual information to the human operator; the human operator then provides tele-operation commands in real-time to the robot. This paper discusses approaches to dense scene reduction of the data required to transmit to a human-in-the loop as well as the challenges associated with them. In addition, the complex and unstructured nature of real-world environments increases the need for tele-operation. Furthermore, many environments reduce the bandwidth and increase the latency of the link. Ultimately, worsening conditions will cause the tele-operation control process to break down, rendering the robot ineffective. In a worst-case scenario, extreme conditions causing a complete loss-of-communications could result in mission failure and loss of the vehicle.

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“…Local area processing method based on histogram shaping and adaptive Wiener filtering for noise removal are used together for contrast enhancement in [3]. In [4], edges are enhanced by adaptive thresholding using error-diffusion algorithm.…”

Section: Related Workmentioning

confidence: 99%

Adaptive image enhancement based on clustering of wavelet coefficients for infrared sea surveillance systems

Karali

Okman

Aytaç

2011

Infrared Physics & Technology

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A multiresolution approach to image enhancement via histogram shaping and adaptive Wiener filtering

Cited by 6 publications

References 0 publications

A Dual Convolutional Neural Network with Attention Mechanism for Thermal Infrared Image Enhancement

A Dual Convolutional Neural Network with Attention Mechanism for Thermal Infrared Image Enhancement

Technologies for low-bandwidth high-latency unmanned ground vehicle control

Adaptive image enhancement based on clustering of wavelet coefficients for infrared sea surveillance systems

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