Learning in-place residual homogeneity for single image detail enhancement

Jiang, He; Asad, Mujtaba; Huang, Xiaolin; Yang, Jie

doi:10.1117/1.jei.29.4.043016

Cited by 7 publications

(18 citation statements)

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“…In this paper, several common infrared image enhancement methods are selected for comparison, including HE, contrast limited adaptive histogram equalization (CLAHE) [ 4 ], dominant orientation-based texture histogram equalization (DOTHE) [ 5 ], adaptive histogram partition and brightness correction approach (AHPBC) [ 6 ], and learning in place residual homogeneity-master (LPRHM) [ 27 ].…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

“…Enhancing the detail layer of interest through multi-image layering has also been widely used. The learning in-place residual homogeneity for single image detail enhancement (LPRHM) [ 27 ] algorithm uses the in-place residual homogeneity to extract detail layers and enhance texture features. Wang, ZJ et al proposed a layered image enhancement method based on improved guided filtering and compressed the high dynamic range of the image through the distribution information of the histogram, preserving details and suppressing noise [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image Enhancement of Maritime Infrared Targets Based on Scene Discrimination

Jiang

Dong

Liang

2022

Sensors

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“…In [9], the author adopts Zero-order Filter (ZF) to fit residual features, but the low order makes its fitting ability limited. In view of this, the author of [5,6] proposes a detail enhancement algorithm based on In-Place Residual Homogeneity (IPRH) and obtains the image detail layer by means of searching and matching. However, the searching process is a greedy mechanism, which makes the algorithm converge to the local optimal solution with a high probability.…”

Section: Introductionmentioning

confidence: 99%

“…In our proposed work, we still use searching and matching technique to get the residual layer, that is, the rough detail layer, but unlike the methods in [5,6], the searching and matching process will be analogous to the process of cooling a thermodynamic system. With the help of the Metropolis theorem, a thermodynamic system can find where the lowest point of internal energy locates.…”

Section: Introductionmentioning

confidence: 99%

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Metropolis Theorem and Its Applications in Single Image Detail Enhancement

He¹,

Asad²,

Liu³

et al. 2023

Preprint

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Traditional image detail enhancement is local filter-based or global filter-based. In both approaches, the original image is first divided into the base layer and the detail layer, and then the enhanced image is obtained by amplifying the detail layer. Our method is different, and its innovation lies in the special way to get the image detail layer. The detail layer in our method is obtained by updating the residual features, and the updating mechanism is usually based on searching and matching similar patches. However, due to the diversity of image texture features, perfect matching is often not possible. In this paper, the process of searching and matching is treated as a thermodynamic process, where the Metropolis theorem can minimize the internal energy and get the global optimal solution of this task, that is, to find a more suitable feature for a better detail enhancement performance. Extensive experiments have proven that our algorithm can achieve better results in quantitative metrics testing and visual effects evaluation. The source code can be obtained from the link.

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