Adaptive Image Enhancement Research Articles

Domain adaptive multi-frequency underwater image enhancement network

Domain adaptive multi-frequency underwater image enhancement network

A three-stage framework for accurate detection of high-speed train body paint film defects

Detecting paint film defects on high-speed train (HST) body is a crucial step towards zero-defect manufacturing (ZDM). The effectiveness of defect detection in industrial environments, however, is significantly impacted by factors such as high reflection noise, weak defect features, small defect size, and various defect scales, resulting in unsatisfactory detection accuracy. To address these challenges, this paper proposes a technical framework for the accurate detection of paint film defects on HST body, encompassing three stages. A reflection detection removal method that takes into account the reflection transition region as well as the preservation of defect features is at first used to reduce reflection noise interference. Then a lightness-weighted adaptive image enhancement algorithm based on the fractional order differentiation is utilized to enhance the features of paint film defects while avoiding the introduction of reflection noise. Finally, an improved YOLOv5 algorithm, incorporating attention mechanism feature extraction and multi-scale feature fusion, is employed to detect and classify paint film defects. Experimental results demonstrate that the proposed framework effectively reduces the reflection in paint film images, and enhances the contrast between paint film defects and the background, with a mean average precision (mAP) for defect detection reaching up to 90.13%. This work serves as a valuable reference for the automated detection of defects arising in the painting process of HST body.

Adaptive pseudo-colour image enhancement algorithm for high-grey X-ray film based on pixel self-transformation

Adaptive pseudo-colour image enhancement algorithm for high-grey X-ray film based on pixel self-transformation

Natural low-illumination image enhancement based on dual-channel prior information

This paper proposes an adaptive image enhancement method that aims to effectively restore the brightness, detail, and natural color of various low-illumination images. To be specific, the method first constructs the initial dual-channel illumination map of the image. Next, the optimal illumination correction coefficient is calculated by the prior information entropy of the initial illumination map, which helps to correct potentially erroneous illumination estimates. To restore the illumination, gamma correction is used with the optimal illumination correction coefficient. Finally, an improved perfect reflection constraint model is used to restore the color of the image. Both visual analysis and quantitative comparison with state-of-the-art methods demonstrate the effectiveness of the method in terms of brightness adjustment, detail recovery, and color restoration.

Multi-scale adaptive low-light image enhancement based on deep learning

Multi-scale adaptive low-light image enhancement based on deep learning

NUAM-Net: A Novel Underwater Image Enhancement Attention Mechanism Network

Vision-based underwater exploration is crucial for marine research. However, the degradation of underwater images due to light attenuation and scattering poses a significant challenge. This results in the poor visual quality of underwater images and impedes the development of vision-based underwater exploration systems. Recent popular learning-based Underwater Image Enhancement (UIE) methods address this challenge by training enhancement networks with annotated image pairs, where the label image is manually selected from the reference images of existing UIE methods since the groundtruth of underwater images do not exist. Nevertheless, these methods encounter uncertainty issues stemming from ambiguous multiple-candidate references. Moreover, they often suffer from local perception and color perception limitations, which hinder the effective mitigation of wide-range underwater degradation. This paper proposes a novel NUAM-Net (Novel Underwater Image Enhancement Attention Mechanism Network) that addresses these limitations. NUAM-Net leverages a probabilistic training framework, measuring enhancement uncertainty to learn the UIE mapping from a set of ambiguous reference images. By extracting features from both the RGB and LAB color spaces, our method fully exploits the fine-grained color degradation clues of underwater images. Additionally, we enhance underwater feature extraction by incorporating a novel Adaptive Underwater Image Enhancement Module (AUEM) that incorporates both local and long-range receptive fields. Experimental results on the well-known UIEBD benchmark demonstrate that our method significantly outperforms popular UIE methods in terms of PSNR while maintaining a favorable Mean Opinion Score. The ablation study also validates the effectiveness of our proposed method.

Multi-degradation-adaptation network for fundus image enhancement with degradation representation learning

Fundus image quality serves a crucial asset for medical diagnosis and applications. However, such images often suffer degradation during image acquisition where multiple types of degradation can occur in each image. Although recent deep learning based methods have shown promising results in image enhancement, they tend to focus on restoring one aspect of degradation and lack generalisability to multiple modes of degradation. We propose an adaptive image enhancement network that can simultaneously handle a mixture of different degradations. The main contribution of this work is to introduce our Multi-Degradation-Adaptive module which dynamically generates filters for different types of degradation. Moreover, we explore degradation representation learning and propose the degradation representation network and Multi-Degradation-Adaptive discriminator for our accompanying image enhancement network. Experimental results demonstrate that our method outperforms several existing state-of-the-art methods in fundus image enhancement. Code will be available at https://github.com/RuoyuGuo/MDA-Net.

MSAug: Multi-Strategy Augmentation for rare classes in semantic segmentation of remote sensing images

Recently, remote sensing images have been widely used in many scenarios, gradually becoming the focus of social attention. Nevertheless, the limited annotation of scarce classes severely reduces segmentation performance. This phenomenon is more prominent in remote sensing image segmentation. Given this, we focus on image fusion and model feedback, proposing a multi-strategy method called MSAug to address the remote sensing imbalance problem. Firstly, we crop rare class images multiple times based on prior knowledge at the image patch level to provide more balanced samples. Secondly, we design an adaptive image enhancement module at the model feedback level to accurately classify rare classes at each stage and dynamically paste and mask different classes to further improve the model’s recognition capabilities. The MSAug method is highly flexible and can be plug-and-play. Experimental results on remote sensing image segmentation datasets show that adding MSAug to any remote sensing image semantic segmentation network can bring varying degrees of performance improvement.

Lit me up: A reference free adaptive low light image enhancement for in-the-wild conditions

Images captured with different devices in uneven conditions (e.g., invariable lighting, low lighting, weather changes, exposure time, etc.) often lead to low image visibility and poor color and contrast, affecting the performance of computer vision and pattern recognition applications. The pre-trained convolutional neural networks (CNNs) solely rely on the training data and lack adaptation due to the uncertainty in the lighting conditions. Moreover, capturing large-scale datasets to train CNNs also raises the computational complexity and overall cost. This work integrates the knowledge and data and proposes a two-stage Uneven-to-Enliven network (U2E-Net), which rapidly learns to see in uneven conditions. A multiple-layered Uneven network learns to distinguish the reflection and illumination in the input images, and an encoder–decoder-based Enliven-Net contextualizes the illumination information. A key component in such ill-posed problems is to obtain information from priors and pairs; however, we present the compelling idea of information trade-off followed by decomposition consistency, thereby progressively improving the visual quality with the subsequent enhancement operations. To this end, we proposed a two-faceted framework that can work without depending on the data type. A novel color and contrast preservation strategy (CPS) is proposed following the decomposition of input data. CPS is integrated within the network to extract contrast in the darkest background regions.

Multi-feature Learning Adaptive Network for Underwater Image Enhancement

Underwater image enhancement faces variety of challenges owing to the diversity of underwater scenes (viewed as water types) and the rich multi-frequency information. To deal with these challenges, this paper proposes a multi-feature learning adaptive underwater image enhancement network comprising an adaptive module and a dual-layer synchronous enhancement network. First, we design an adaptive module which enables the determination of water type inside the model and eliminates the negative effect of water type diversity by building water type related features. Then, the model learns high-frequency and low-frequency features through a dual-layer synchronous enhancement network to extract more comprehensive information. Finally, the outputs of the dual-layer network are merged to obtain more realistic underwater enhanced images. Numerous experiments have shown that the proposed method outperforms the comparison method for visual perception and assessment metrics.

Enhancing and Adapting in the Clinic: Source-Free Unsupervised Domain Adaptation for Medical Image Enhancement.

Medical imaging provides many valuable clues involving anatomical structure and pathological characteristics. However, image degradation is a common issue in clinical practice, which can adversely impact the observation and diagnosis by physicians and algorithms. Although extensive enhancement models have been developed, these models require a well pre-training before deployment, while failing to take advantage of the potential value of inference data after deployment. In this paper, we raise an algorithm for source-free unsupervised domain adaptive medical image enhancement (SAME), which adapts and optimizes enhancement models using test data in the inference phase. A structure-preserving enhancement network is first constructed to learn a robust source model from synthesized training data. Then a teacher-student model is initialized with the source model and conducts source-free unsupervised domain adaptation (SFUDA) by knowledge distillation with the test data. Additionally, a pseudo-label picker is developed to boost the knowledge distillation of enhancement tasks. Experiments were implemented on ten datasets from three medical image modalities to validate the advantage of the proposed algorithm, and setting analysis and ablation studies were also carried out to interpret the effectiveness of SAME. The remarkable enhancement performance and benefits for downstream tasks demonstrate the potential and generalizability of SAME. The code is available at https://github.com/liamheng/Annotation-free-Medical-Image-Enhancement.

Accurate Recognition of Jujube Tree Trunks Based on Contrast Limited Adaptive Histogram Equalization Image Enhancement and Improved YOLOv8

The accurate recognition of tree trunks is a prerequisite for precision orchard yield estimation. Facing the practical problems of complex orchard environment and large data flow, the existing object detection schemes suffer from key issues such as poor data quality, low timeliness and accuracy, and weak generalization ability. In this paper, an improved YOLOv8 is designed on the basis of data flow screening and enhancement for lightweight jujube tree trunk accurate detection. Firstly, the key frame extraction algorithm was proposed and utilized to efficiently screen the effective data. Secondly, the CLAHE image data enhancement method was proposed and used to enhance the data quality. Finally, the backbone of the YOLOv8 model was replaced with a GhostNetv2 structure for lightweight transformation, also introducing the improved CA_H attention mechanism. Extensive comparison and ablation results show that the average precision of the quality-enhanced dataset over that of the original dataset increases from 81.2% to 90.1%, and the YOLOv8s-GhostNetv2-CA_H model proposed in this paper reduces the model size by 19.5% compared to that of the YOLOv8s base model, with precision increasing by 2.4% to 92.3%, recall increasing by 1.4%, mAP@0.5 increasing by 1.8%, and FPS being 17.1% faster.

High-precision retinal blood vessel segmentation based on a multi-stage and dual-channel deep learning network.

Objective.The high-precision segmentation of retinal vessels in fundus images is important for the early diagnosis of ophthalmic diseases. However, the extraction for microvessels is challenging due to their characteristics of low contrast and high structural complexity. Although some works have been developed to improve the segmentation ability in thin vessels, they have only been successful in recognizing small vessels with relatively high contrast.Approach.Therefore, we develop a deep learning (DL) framework with a multi-stage and dual-channel network model (MSDC_NET) to further improve the thin-vessel segmentation with low contrast. Specifically, an adaptive image enhancement strategy combining multiple preprocessing and the DL method is firstly proposed to elevate the contrast of thin vessels; then, a two-channel model with multi-scale perception is developed to implement whole- and thin-vessel segmentation; and finally, a series of post-processing operations are designed to extract more small vessels in the predicted maps from thin-vessel channels.Main results.Experiments on DRIVE, STARE and CHASE_DB1 demonstrate the superiorities of the proposed MSDC_NET in extracting more thin vessels in fundus images, and quantitative evaluations on several parameters based on the advanced ground truth further verify the advantages of our proposed DL model. Compared with the previous multi-branch method, the specificity and F1score are improved by about 2.18%, 0.68%, 1.73% and 2.91%, 0.24%, 8.38% on the three datasets, respectively.Significance.This work may provide richer information to ophthalmologists for the diagnosis and treatment of vascular-related ophthalmic diseases.

AIE-YOLO: Effective object detection method in extreme driving scenarios via adaptive image enhancement.

The widespread research and implementation of visual object detection technology have significantly transformed the autonomous driving industry. Autonomous driving relies heavily on visual sensors to perceive and analyze the environment. However, under extreme weather conditions, such as heavy rain, fog, or low light, these sensors may encounter disruptions, resulting in decreased image quality and reduced detection accuracy, thereby increasing the risk for autonomous driving. To address these challenges, we propose adaptive image enhancement (AIE)-YOLO, a novel object detection method to enhance road object detection accuracy under extreme weather conditions. To tackle the issue of image quality degradation in extreme weather, we designed an improved adaptive image enhancement module. This module dynamically adjusts the pixel features of road images based on different scene conditions, thereby enhancing object visibility and suppressing irrelevant background interference. Additionally, we introduce a spatial feature extraction module to adaptively enhance the model's spatial modeling capability under complex backgrounds. Furthermore, a channel feature extraction module is designed to adaptively enhance the model's representation and generalization abilities. Due to the difficulty in acquiring real-world data for various extreme weather conditions, we constructed a novel benchmark dataset named extreme weather simulation-rare object dataset. This dataset comprises ten types of simulated extreme weather scenarios and is built upon a publicly available rare object detection dataset. Extensive experiments conducted on the extreme weather simulation-rare object dataset demonstrate that AIE-YOLO outperforms existing state-of-the-art methods, achieving excellent detection performance under extreme weather conditions.

An image augmentation‐based ice monitoring method for safe navigation of polar ships

AbstractThe navigation safety of polar ships depends on accurate monitoring of ice conditions along the route. Complex polar environments but scarce ice datasets pose challenges for deep learning‐based object detection methods. To address this issue, we propose an ice monitoring method based on polar environment adaptive image enhancement. This approach adaptively augments the ice dataset by simulating polar environments such as snowflakes and aurora to improve the training precision of ice detection models in harsh polar environments and meet the needs of safe navigation of polar ships. First, an image augmentation method based on randomly generated snowflake textures is proposed to address the difficulty of covering polar storm scenarios in existing ice datasets, which leads to weak generalization of ice monitoring models. Second, targeting the problem that the boundary between ice and background is confused by aurora phenomena, which severely reduces the ice monitoring accuracy, we propose an image augmentation method based on aurora simulations to improve the ice monitoring performance of trained models under aurora. Finally, an adaptive image augmentation strategy based on the brightness‐contrast threshold is proposed to optimize the snowflake density and aurora intensity in the augmented images to improve the discrimination of ice boundaries. Experimental results demonstrate the effectiveness of the proposed method, which improves the ice state detection accuracy from 0.640 to 0.950, thus providing a reliable guarantee for the safe navigation of polar ships.

Channel adaptive underwater image enhancement algorithm based on pixel level

Channel adaptive underwater image enhancement algorithm based on pixel level

Adaptive Image Enhancement Method for Coal-Mine Underground Image Based on No-Reference Quality Evaluation

Adaptive Image Enhancement Method for Coal-Mine Underground Image Based on No-Reference Quality Evaluation

A Method for Adaptively Enhancing Foggy Low‐Illumination Image

Due to the influence of the foggy weather, the increase of dust particles in the air affects the atmospheric scattering, resulting in the image degradation in the foggy low‐illumination environment, such as blurred content and low contrast. This study proposes an adaptive image enhancement method for foggy low‐illuminance image based on the proposed adaptive gamma correction and adaptive multiscale retinex (MSR). The proposed enhancement method firstly converts the foggy low‐illumination image into HSI space and then performs the proposed adaptive gamma correction on its luminance component through estimating the light component of the image based on the retinex theory. After converting the obtained intermediate image back into RGB space, the proposed adaptive MSR is performed on it to obtain the final enhanced image. For the proposed adaptive gamma correction and adaptive MSR, an effective fitness function is designed to select the optimal scale value by virtue of particle swarm optimization (PSO) algorithm. The experiments show that the proposed method has better effect on improving the sharpness and contrast of the image, compared with the typical algorithms. The processing results of the proposed method have an average increase of 12.16% in information entropy, an average increase of 220.52% in average gradient, and an average increase of 111.63% in visual contrast, compared with the original foggy image.

Adaptive high-gray image enhancement algorithm based on logarithmic mapping and simulated exposure

In the fields of industry, security, military and other areas, high gray-level low contrast images have been widely used. However, due to the large dynamic range of gray level, high noise, blurry detail information and low contrast, it brings difficulties to subsequent image enhancement, target detection and other processes. Ordinary image enhancement algorithms are only suitable for conventional 8-bit images, which are not effective to deal with high-gray and low-contrast images. To address these issues, this paper proposed an adaptive high-gray image enhancement algorithm based on logarithmic mapping and simulated exposure, which was used for processing X-ray film images, infrared images, SAR images and other high gray-level low contrast images. Firstly, a detailed algorithm model construction method was provided for high gray-level low contrast images. Secondly, in order to verify the universality of the algorithm proposed in this paper, qualitative and quantitative experimental analyses were carried out using X-ray film images, infrared images and SAR images respectively. Finally, taking the most complex infrared image in the application environment as an example, the images, enhanced by the algorithm proposed in this paper, were sent to the YOLOv8 neural network for target detection, which improved the detection accuracy. The experiment shows that the algorithm, proposed in this paper, can significantly improve the image quality of high gray-level low contrast images, the detail information is obvious and the contrast is significantly improved. Various indicators are far higher than those of traditional algorithms. At the same time, this algorithm can provide reliable technical support for applications, such as target detection and target tracking in this field.

ARODNet: adaptive rain image enhancement object detection network for autonomous driving in adverse weather conditions

ARODNet: adaptive rain image enhancement object detection network for autonomous driving in adverse weather conditions