Two adaptive enhancement algorithms for high gray-scale RAW infrared images based on multi-scale fusion and chromatographic remapping

Li, Jianheng; Li, Xiaobin; Wang, Hongxi; Wang, Peng; Li, Liangliang; Shu, Lei; Lv, Zhigang

doi:10.1016/j.infrared.2023.104774

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…Features such as the pixel gray-scale average (as an indicator of bone marrow fat and the BMD) [28], the fractal dimension (using the Boxing count method), and the gray-level co-occurrence matrix were also derived from texture analysis (for further information on the image segmentation and texture analysis refer to the work of Larroza et al [29]). A Then, statistical functions such as the minimum, maximum, mean, variance, elongation, skewness, and first and second moments of the signal (moment1 and moment2 in Table 1) of these descriptors were calculated.…”

Section: Iou S T S T   mentioning

confidence: 99%

Section: Classification Using Decision Treementioning

confidence: 99%

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Qualitative Classification of Proximal Femoral Bone Using Geometric Features and Texture Analysis in Collected MRI Images for Bone Density Evaluation

Najafi,

Yousefi Rezaii,

Danishvar

et al. 2023

Sensors

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The aim of this study was to use geometric features and texture analysis to discriminate between healthy and unhealthy femurs and to identify the most influential features. We scanned proximal femoral bone (PFB) of 284 Iranian cases (21 to 83 years old) using different dual-energy X-ray absorptiometry (DEXA) scanners and magnetic resonance imaging (MRI) machines. Subjects were labeled as “healthy” (T-score > −0.9) and “unhealthy” based on the results of DEXA scans. Based on the geometry and texture of the PFB in MRI, 204 features were retrieved. We used support vector machine (SVM) with different kernels, decision tree, and logistic regression algorithms as classifiers and the Genetic algorithm (GA) to select the best set of features and to maximize accuracy. There were 185 participants classified as healthy and 99 as unhealthy. The SVM with radial basis function kernels had the best performance (89.08%) and the most influential features were geometrical ones. Even though our findings show the high performance of this model, further investigation with more subjects is suggested. To our knowledge, this is the first study that investigates qualitative classification of PFBs based on MRI with reference to DEXA scans using machine learning methods and the GA.

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Section: Iou S T S T   mentioning

confidence: 99%

Section: Classification Using Decision Treementioning

confidence: 99%

Qualitative Classification of Proximal Femoral Bone Using Geometric Features and Texture Analysis in Collected MRI Images for Bone Density Evaluation

Najafi,

Yousefi Rezaii,

Danishvar

et al. 2023

Sensors

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“…Overall, high-dynamic-range image compression algorithms can be classified into traditional mapping-based algorithms, gradient domain-based compression algorithms, and image layering-based compression algorithms. Besides these, there are also a few methods [2,3] that cannot be classified into any of these three classes. Different algorithms are applicable for various application contexts and vary in their complexity and performance.…”

Section: Introductionmentioning

confidence: 99%

A Low-Delay Dynamic Range Compression and Contrast Enhancement Algorithm Based on an Uncooled Infrared Sensor with Local Optimal Contrast

Zhu,

Zhou,

Jin

et al. 2023

Sensors

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Real-time compression of images with a high dynamic range into those with a low dynamic range while preserving the maximum amount of detail is still a critical technology in infrared image processing. We propose a dynamic range compression and enhancement algorithm for infrared images with local optimal contrast (DRCE-LOC). The algorithm has four steps. The first involves blocking the original image to determine the optimal stretching coefficient by using the information of the local block. In the second, the algorithm combines the original image with a low-pass filter to create the background and detailed layers, compressing the background layer with a dynamic range of adaptive gain, and enhancing the detailed layer for the visual characteristics of the human eye. Third, the original image was used as input, the compressed background layer was used as a brightness-guided image, and the local optimal stretching coefficient was used for dynamic range compression. Fourth, an 8-bit image was created (from typical 14-bit input) by merging the enhanced details and the compressed background. Implemented on FPGA, it used 2.2554 Mb of Block RAM, five dividers, and a root calculator with a total image delay of 0.018 s. The study analyzed mainstream algorithms in various scenarios (rich scenes, small targets, and indoor scenes), confirming the proposed algorithm’s superiority in real-time processing, resource utilization, preservation of the image’s details, and visual effects.

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Effect of color palette and text encoding on infrared thermal imaging perception with various screen resolution accuracy

Tian,

Xie,

Tang

2025

Displays

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Two adaptive enhancement algorithms for high gray-scale RAW infrared images based on multi-scale fusion and chromatographic remapping

Cited by 6 publications

References 19 publications

Qualitative Classification of Proximal Femoral Bone Using Geometric Features and Texture Analysis in Collected MRI Images for Bone Density Evaluation

Qualitative Classification of Proximal Femoral Bone Using Geometric Features and Texture Analysis in Collected MRI Images for Bone Density Evaluation

A Low-Delay Dynamic Range Compression and Contrast Enhancement Algorithm Based on an Uncooled Infrared Sensor with Local Optimal Contrast

Effect of color palette and text encoding on infrared thermal imaging perception with various screen resolution accuracy

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