Super-efficient enhancement algorithm for infrared night vision imaging system

Ashiba, Huda Ibrahim; Ashiba, M. I.

doi:10.1007/s11042-020-09928-w

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…The existing active anti-halation methods include sticking polarizing film on the front windscreen [2], infrared night vision imaging [3][4][5], an array of CCD image sensors with a pixel integration time independently controllable [6], two visible image fusions with different light integration times [7], etc. Among them, multi-exposure image fusion [8] and infrared and visible image fusion [9,10] have lower halation retention and better visual effects.…”

Section: Introductionmentioning

confidence: 99%

“…(2) The low-frequency sequence generation model is constructed to generate image sequences with different degrees of halation elimination. (3) The sequence synthesis based on visual information maximization is proposed. According to the estimated illuminance for image sequences, the membership function of visual information maximization assigns a large weight to the areas with good brightness to generate a fusion image conforming to human visual perception.…”

Section: Introductionmentioning

confidence: 99%

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Night Vision Anti-Halation Algorithm of Different-Source Image Fusion Based on Low-Frequency Sequence Generation

2023

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The abuse of high beam lights dazzles the opposite drivers when the vehicles meet at night, which can easily cause traffic accidents. The existing night vision anti-halation algorithms based on different-source image fusion can eliminate halation and obtain fusion images with rich color and details. However, the algorithms mistakenly eliminate some high-brightness important information. In order to address the problem, a night vision anti-halation algorithm based on low-frequency sequence generation is proposed. The low-frequency sequence generation model is constructed to generate image sequences with different degrees of halation elimination. According to the estimated illuminance for image sequences, the proposed sequence synthesis based on visual information maximization assigns a large weight to the areas with good brightness so as to obtain the fusion image without halation and with rich details. In four typical halation scenes covering most cases of night driving, the proposed algorithm effectively eliminates halation while retaining useful high-brightness information and has better universality than the other seven advanced comparison algorithms. The experimental results show that the fusion image obtained by the proposed algorithm is more suitable for human visual perception and helps to improve night driving safety.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Night Vision Anti-Halation Algorithm of Different-Source Image Fusion Based on Low-Frequency Sequence Generation

2023

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“…AHPBC includes three stages: segmentation, local detail enhancement, and noise suppression. To enhance contrast and details of interest, Ashiba et al also proposed merging gamma correction with histogram matching [ 7 ]. The characteristic of this kind of algorithm is that it can significantly improve the contrast of the image and increase the dynamic range of the gray level of the infrared image.…”

Section: Introductionmentioning

confidence: 99%

Image Enhancement of Maritime Infrared Targets Based on Scene Discrimination

Jiang

Dong

Liang

2022

Sensors

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Infrared image enhancement technology can effectively improve the image quality and enhance the saliency of the target and is a critical component in the marine target search and tracking system. However, the imaging quality of maritime infrared images is easily affected by weather and sea conditions and has low contrast defects and weak target contour information. At the same time, the target is disturbed by different intensities of sea clutter, so the characteristics of the target are also different, which cannot be processed by a single algorithm. Aiming at these problems, the relationship between the directional texture features of the target and the roughness of the sea surface is deeply analyzed. According to the texture roughness of the waves, the image scene is adaptively divided into calm sea surface and rough sea surface. At the same time, through the Gabor filter at a specific frequency and the gradient-based target feature extraction operator proposed in this paper, the clutter suppression and feature fusion strategies are set, and the target feature image of multi-scale fusion in two types of scenes are obtained, which is used as a guide image for guided filtering. The original image is decomposed into a target and a background layer to extract the target features and avoid image distortion. The blurred background around the target contour is extracted by Gaussian filtering based on the potential target region, and the edge blur caused by the heat conduction of the target is eliminated. Finally, an enhanced image is obtained by fusing the target and background layers with appropriate weights. The experimental results show that, compared with the current image enhancement method, the method proposed in this paper can improve the clarity and contrast of images, enhance the detectability of targets in distress, remove sea surface clutter while retaining the natural environment features in the background, and provide more information for target detection and continuous tracking in maritime search and rescue.

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“…Considering the above disadvantages of HE, some improved algorithms are designed, including enhancement method using an adjacent-blocks-based modification for local HE [8], contrast limited adaptive histogram equalization (CLAHE) [9], adaptive gamma correction based on cumulative HE approach [10] and adaptive histogram equalization and brightness correction (AHPBC) [11]. To enhance the contrast and details, Ashiba et al improve and propose three ideas for infrared enhancement [12]: The first approach makes use of gamma correction with histogram matching, the second one depends on hybrid gamma correction with CLAHE, the third method utilises a trilateral enhancement which includes three stages, segmentation, enhancement and sharpening. Those methods would well highlight the target areas, but they would create some negative effects, such as enhanced background and amplified noise.…”

Section: Introductionmentioning

confidence: 99%

“…To enhance the contrast and details, Ashiba et al. improve and propose three ideas for infrared enhancement [12]: The first approach makes use of gamma correction with histogram matching, the second one depends on hybrid gamma correction with CLAHE, the third method utilises a trilateral enhancement which includes three stages, segmentation, enhancement and sharpening. Those methods would well highlight the target areas, but they would create some negative effects, such as enhanced background and amplified noise.…”

Section: Introductionmentioning

confidence: 99%

Infrared imaging enhancement through local window‐based saliency extraction with spatial weight

Zhao

et al. 2021

IET Image Processing

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Infrared image enhancement is an effective way to solve contrast reduction or details degradation in infrared imagery. An infrared enhancement approach based on local saliency extraction is proposed here. First, saliency maps are extracted within a local window by combining spatial weight. Second, with the change of the window size, potential targets and details in different sizes can be extracted. Considering window sizes as the scales, the saliency maps are obtained and infrared images are enhanced at different scales, and finally, multi-scale fusion is used to achieve the enhancement. Eight popular infrared enhancement approaches are introduced for comparison. Subjective qualitative observation experiments show that our strategy based on local saliency analysis and multi-scale fusion can well extract potential targets and areas of varying size from the source images with different sizes, to obtain a good enhancement effect. Meanwhile, we introduce six objective evaluation methods to measure the results, and the evaluation data to prove the effectiveness of the proposed algorithm. The experiments also indicate the real-time processing capability of the proposed method. The proposed method is finally well applied in the hardware system and shows its good performance.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Super-efficient enhancement algorithm for infrared night vision imaging system

Cited by 5 publications

References 21 publications

Night Vision Anti-Halation Algorithm of Different-Source Image Fusion Based on Low-Frequency Sequence Generation

Night Vision Anti-Halation Algorithm of Different-Source Image Fusion Based on Low-Frequency Sequence Generation

Image Enhancement of Maritime Infrared Targets Based on Scene Discrimination

Infrared imaging enhancement through local window‐based saliency extraction with spatial weight

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