Efficient Image Enhancement Research Articles

MWRD (Mamba Wavelet Reverse Diffusion)—An Efficient Fundus Image Enhancement Network Based on an Improved State-Space Model

This study introduces an efficient fundus image enhancement framework based on an improved Mamba model and the Denoising Diffusion Probabilistic Model (DDPM). By integrating wavelet transform for local feature extraction and applying a reverse diffusion process, this approach significantly improves the effectiveness and efficiency of enhancing low-quality fundus images. The model achieves high-precision enhancement of retinal vessel details while substantially reducing the number of parameters. Comprehensive tests on publicly available datasets show that the suggested approach surpasses various advanced low-light image enhancement methods in both quantitative and qualitative assessments.

Efficient MRI image enhancement by improved denoising techniques for better skull stripping using attention module-based convolution neural network

ABSTRACT Anatomical structure preservation throughout the denoising process is a challenge in the domain of medical imaging. The Rician noise introduced through the acquisition procedure by the Magnetic Resonance Imaging (MRI) scanner distorts the images. In this study, denoising using Wavelet-based Non-Local Median Filter (WBNLMF) and a novel contrast-enhancement method termed Improved Minimum Intensity Error Intuitionistic Fuzzy Contrast Enhancement (IMIEIFCET) is suggested. This methodology gives superior results while maintaining the edges and the brightness of the original image. An Attention Module-based Convolution Neural Network (AM-CNN) is suggested in the research as a methodology for skull stripping from MRI data. With a mean Dice coefficient of 0.998, a Sensitivity of 0.9975, and a Specificity of 0.9985, the proposed network exhibits result that are comparable to those of the specified Deep Learning (DL)-based technique.

UAV Target Tracking Algorithm Based on Illumination Adaptation and Future Awareness in Low Illumination Scenes

Aiming at problems such as tracking failure caused by illumination changes often encountered during unmanned aerial vehicle (UAV) tracking, a target tracking algorithm with illumination adaptive and future-aware correlation filters is proposed based on the background-aware correlation filters (BACF) algorithm, which realizes reliable UAV tracking tasks at night. First, the dark scene is recognized, and an efficient image enhancement module is used to enhance the brightness of low illumination images. Then a future-aware module is constructed to train the tracking model using the contextual information of the target in the next frame for better robustness. Finally, the model updating stage involves adaptive filter updating and adaptive learning rate updating to enhance target tracking precision. The results of the comparison experiments with the state-of-the-art algorithms on the UAVDark135, UAVDT, and DTB70 datasets show that the algorithm in this paper outperforms the state-of-the-art tracking methods and has better tracking performance under light changes and fast motion (FM) scenarios. The tracking speed on a single Central processing unit (CPU) reaches 49 FPS, which satisfies the real-time requirement of UAV tracking.

VELIE: A Vehicle-Based Efficient Low-Light Image Enhancement Method for Intelligent Vehicles.

In Advanced Driving Assistance Systems (ADAS), Automated Driving Systems (ADS), and Driver Assistance Systems (DAS), RGB camera sensors are extensively utilized for object detection, semantic segmentation, and object tracking. Despite their popularity due to low costs, RGB cameras exhibit weak robustness in complex environments, particularly underperforming in low-light conditions, which raises a significant concern. To address these challenges, multi-sensor fusion systems or specialized low-light cameras have been proposed, but their high costs render them unsuitable for widespread deployment. On the other hand, improvements in post-processing algorithms offer a more economical and effective solution. However, current research in low-light image enhancement still shows substantial gaps in detail enhancement on nighttime driving datasets and is characterized by high deployment costs, failing to achieve real-time inference and edge deployment. Therefore, this paper leverages the Swin Vision Transformer combined with a gamma transformation integrated U-Net for the decoupled enhancement of initial low-light inputs, proposing a deep learning enhancement network named Vehicle-based Efficient Low-light Image Enhancement (VELIE). VELIE achieves state-of-the-art performance on various driving datasets with a processing time of only 0.19 s, significantly enhancing high-dimensional environmental perception tasks in low-light conditions.

Efficient Image Enhancement via Representative Color Transform

Efficient Image Enhancement via Representative Color Transform

A Multi-scale feature modulation network for efficient underwater image enhancement

Many strategies have been proposed for underwater image enhancement. However, they are often incompatible with underwater remotely operated vehicle platforms with low memory and computational power. In this paper, we address this problem by proposing a simple yet effective multi-scale feature modulation network to achieve a better trade-off between the efficiency of the model and the performance of the reconstruction. Specifically, we develop a multi-scale modulation module on top of a Visual Transformer (VIT)-like block. Within this module, we extract the features of the input image using a Multi-scale Spatial Feature Module (MSFM) to dynamically select representative features in the feature space of the image. Since the MSFM block handles the image features from a spatial point of view of the image, we further introduce the channel mixing module to perform the channel mixing. Extensive experimental results show that the proposed method is 8.5 times smaller in terms of network parameters than the state-of-the-art underwater image enhancement methods (e.g., Semi-uir), and achieves essentially the same performance with less computational cost. The code is available at https://github.com/zhengshijian1993/MFMN.

Relevance gradient descent for parameter optimization of image enhancement

Machine learning plays a pivotal role in constructing learning models and leveraging abundant image data for feature extraction, which is essential for achieving accurate and efficient image enhancement through optimization algorithms. Traditional gradient descent methods are confined to optimizing independent parameters, while recent algorithms neglect the interdependence among parameters, resulting in prolonged iteration times for each method. To address this issue, we propose a novel Correlation-Based Gradient Descent (RGD) algorithm tailored for enhancing low-light images. RGD leverages inter-iteration results for mutual parameter transmission and optimization, employing convergence or oscillation criteria to select the final enhanced image and find the optimal parameter combination for image enhancement, thereby enhancing parameter optimization efficiency. Specifically, each iteration is divided into multiple operations, with each operation adopting different enhancement schemes. During each iteration, the results of different operation parameters are shared, which facilitates the discovery of optimal parameters for low-light image enhancement. Subsequently, corresponding enhancement operations are performed based on this parameter combination to optimize standard indicators. Our experiments on medical and natural low-light scenarios demonstrate that our RGD method achieves a significant reduction in iteration time, with at least a 17% decrease compared to exhaustive loop methods.

UIEOGP: an underwater image enhancement method based on optical geometric properties.

Due to the inconsistent absorption and scattering effects of different wavelengths of light, underwater images often suffer from color casts, blurred details, and low visibility. To address this image degradation problem, we propose a robust and efficient underwater image enhancement method named UIEOGP. It can be divided into the following three steps. First, according to the light attenuation effect presented by Lambert Beer's law, combined with the variance change after attenuation, we estimate the depth of field in the underwater image. Then, we propose a local-based color correction algorithm to address the color cast issue in underwater images, employing the statistical distribution law. Finally, drawing inspiration from the law of light propagation, we propose detail enhancement algorithms, each based on the geometric properties of circles and ellipses, respectively. The enhanced images produced by our method feature vibrant colors, improved contrast, and sharper detail. Extensive experiments show that our method outperforms current state-of-the-art methods. In further experiments, we found that our method is beneficial for downstream tasks of underwater image processing, such as the detection of keypoints and edges in underwater images.

Low-light image enhancement using gamma correction prior in mixed color spaces

In this paper, we propose an efficient and fast low-light image enhancement method using an atmospheric scattering model based on an inverted low-light image. The transmission map is derived as a function of two saturations of the original image in the two color spaces. Due to the difficulty in estimating the saturation of the original image, the transmission map is converted into a function of the average and maximum values of the original image. These two values are estimated from a given low-light image using the gamma correction prior. In addition, a pixel-adaptive gamma value determination algorithm is proposed to prevent under- or over-enhancement. The proposed algorithm is fast because it does not require the training or refinement process. The simulation results show that the proposed low-light enhancement scheme outperforms state-of-the-art approaches regarding both computational simplicity and enhancement efficiency. The code is available on https://github.com/TripleJ2543.

Underwater image enhancement via red channel maximum attenuation prior and multi-scale detail fusion.

The underwater environment poses great challenges, which have a negative impact on the capture and processing of underwater images. However, currently underwater imaging systems cannot adapt to various underwater environments to guarantee image quality. To address this problem, this paper designs an efficient underwater image enhancement approach that gradually adjusts colors, increases contrast, and enhances details. Based on the red channel maximum attenuation prior, we initially adjust the blue and green channels and correct the red channel from the blue and green channels. Subsequently, the maximum and minimum brightness blocks are estimated in multiple channels to globally stretch the image, which also includes our improved guided noise reduction filtering. Finally, in order to amplify local details without affecting the naturalness of the results, we use a pyramid fusion model to fuse local details extracted from two methods, taking into account the detail restoration effect of the optical model. The enhanced underwater image through our method has rich colors without distortion, effectively improved contrast and details. The objective and subjective evaluations indicate that our approach surpasses the state-of-the-art methods currently. Furthermore, our approach is versatile and can be applied to diverse underwater scenes, which facilitates subsequent applications.

An Underwater Image Enhancement Method for a Preprocessing Framework Based on Generative Adversarial Network.

This paper presents an efficient underwater image enhancement method, named ECO-GAN, to address the challenges of color distortion, low contrast, and motion blur in underwater robot photography. The proposed method is built upon a preprocessing framework using a generative adversarial network. ECO-GAN incorporates a convolutional neural network that specifically targets three underwater issues: motion blur, low brightness, and color deviation. To optimize computation and inference speed, an encoder is employed to extract features, whereas different enhancement tasks are handled by dedicated decoders. Moreover, ECO-GAN employs cross-stage fusion modules between the decoders to strengthen the connection and enhance the quality of output images. The model is trained using supervised learning with paired datasets, enabling blind image enhancement without additional physical knowledge or prior information. Experimental results demonstrate that ECO-GAN effectively achieves denoising, deblurring, and color deviation removal simultaneously. Compared with methods relying on individual modules or simple combinations of multiple modules, our proposed method achieves superior underwater image enhancement and offers the flexibility for expansion into multiple underwater image enhancement functions.

The Significance of Metadata and Video Compression for Investigating Video Files on Social Media Forensic

Digital forensics is an essential aspect of cyber security and the investigation of digital crimes. Digital recordings are routinely used as important evidence sources in the identification, analysis, presentation, and reporting of evidence. There has recently been concern that images and videos cannot be used as solid evidence since they may be altered very quickly due to the abundance of technologies available for the gathering and processing of multimedia data. The main goal of this endeavour is to comprehend advanced forensic video analysis methods to assist in criminal investigations. We first propose the acquisition extraction analysis in a forensic video analysis framework that employs efficient video and image enhancement techniques for low-quality video that would be transferred through social media applications and for CCTV footage analysis. The reliability of digital video recordings is essential in forensic science and other criminal investigation fields. Digital video forensic analysis is a technique that constantly faces new challenges. Currently, videos are authenticated using a variety of parameters, including pixel-based analysis, frame rate analysis, bit rate analysis, hash value analysis, and, most importantly, metadata analysis. It was believed that the development of technology required the development of a new method for the verification of digital video recordings. In this review study, we made a novel attempt by reviewing the media. Information and structural analysis of video containers in the MP4 file format have been used to distinguish between real and altered videos.

The Development of a Cost-Effective Imaging Device Based on Thermographic Technology

Thermal vision-based devices are nowadays used in a number of industries, ranging from the automotive industry, surveillance, navigation, fire detection, and rescue missions to precision agriculture. This work describes the development of a low-cost imaging device based on thermographic technology. The proposed device uses a miniature microbolometer module, a 32-bit ARM microcontroller, and a high-accuracy ambient temperature sensor. The developed device is capable of enhancing RAW high dynamic thermal readings obtained from the sensor using a computationally efficient image enhancement algorithm and presenting its visual result on the integrated OLED display. The choice of microcontroller, rather than the alternative System on Chip (SoC), offers almost instantaneous power uptime and extremely low power consumption while providing real-time imaging of an environment. The implemented image enhancement algorithm employs the modified histogram equalization, where the ambient temperature sensor helps the algorithm enhance both background objects near ambient temperature and foreground objects (humans, animals, and other heat sources) that actively emit heat. The proposed imaging device was evaluated on a number of environmental scenarios using standard no-reference image quality measures and comparisons against the existing state-of-the-art enhancement algorithms. Qualitative results obtained from the survey of 11 subjects are also provided. The quantitative evaluations show that, on average, images acquired by the developed camera provide better perception quality in 75% of tested cases. According to qualitative evaluations, images acquired by the developed camera provide better perception quality in 69% of tested cases. The obtained results verify the usability of the developed low-cost device for a range of applications where thermal imaging is needed.

Multiscale Fusion Algorithm for Underwater Image Enhancement Based on Color Preservation

Underwater images regularly exhibit color deviation and low contrast attributed to attenuation and scattering associated with wavelength and distance. For solving these two degradation problems, we propose an efficient underwater image enhancement model, which takes a multiscale fusion strategy as a pivot to concurrently achieve color balance and contrast enhancement. Specifically, we target two core degradation problems through two preprocessing steps. On the one hand, the white balance strategy enhances the appearance of the image by suppressing unnecessary colors to solve the color deviation problem. On the other hand, we employ a color-preserving adaptive histogram equalization (CP-AHE) to obtain an image with higher contrast by jointly processing the three color channels. In the fusion phase, a fusion strategy based on the detail-preserving decomposition of the edge-protected structural blocks is used to converge the white balance and CP-AHE inputs. Extensive experiments on common underwater image datasets have shown the advantages of the proposed method over other state-of-the-art underwater image methods in terms of visual and quantitative evaluation.

FE-GAN: Fast and Efficient Underwater Image Enhancement Model Based on Conditional GAN

The processing of underwater images can vastly ease the difficulty of underwater robots’ tasks and promote ocean exploration development. This paper proposes a fast and efficient underwater image enhancement model based on conditional GAN with good generalization ability using aggregation strategies and concatenate operations to take full advantage of the limited hierarchical features. A sequential network can avoid frequently visiting additional nodes, which is beneficial for speeding up inference and reducing memory consumption. Through the structural re-parameterization approach, we design a dual residual block (DRB) and accordingly construct a hierarchical attention encoder (HAE), which can extract sufficient feature and texture information from different levels of an image, and with 11.52% promotion in GFLOPs. Extensive experiments were carried out on real and artificially synthesized benchmark underwater image datasets, and qualitative and quantitative comparisons with state-of-the-art methods were implemented. The results show that our model produces better images, and has good generalization ability and real-time performance, which is more conducive to the practical application of underwater robot tasks.

Image enhancement in wavelet domain using histogram equalization and median filters

Image enhancement is one of the challenging but crucial methods that is employed in image processing technology for enhancing the visual appearance of images. This paper presents an effective and efficient image enhancement model in which Non Local mean (NLM) filter is used along with Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE) and Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE) techniques. The primary objective of the proposed image enhancement model is to enhance the quality of images by reducing the noise effect in them. To do so, we have selected four different images of Barbara, camera, Lena and Hand whose quality is increased and analyzed on three different noise levels of 15, 20 and 25 respectively. Here, we have used NLM filter which is an advanced filtration technique for denoising the images. Also, MMBEBHE and BPDFHE techniques have been implemented for enhancing the quality of images on different noise levels. The efficiency and usefulness of proposed image enhancement model is examined and validated in MATLAB software under Mean Square Error (MSE) and Peak to Signal Ratio (PSNR) values.

Design and Development of Decorrelation Stretch Technique for Enhancing the Quality of Satellite Images with Improved MSE and UIQI in Comparison with Wiener Filter

Image enhancement is the process of increasing the quality of an image by enhancing certain features of an image and the resultant image is more suitable than the input image. This research is aimed at developing more efficient image enhancement techniques in the spatial domain. Materials and methods: Image enhancement using decorrelation stretch technique (N=30) is proposed in this work, and this method is compared with Wiener filter (N=30). The sample size used for both groups is 30. Results: The quality enhanced image is measured using Mean Square Error (MSE) and Universal Image Quality Index measure (UIQI). High UIQI and low MSE indicates the better enhanced image. The parameters of MSE (db) and UIQI are having higher values than the parameters of Wiener filter. The mean MSE value of decorrelation is 4.2715 db. Mean MSE of wiener filter is 32.0476 db. The mean UIQI value of decorrelation is 0.5640 db and that of Wiener filter is 0.5766 db. Conclusion: Image enhancement using decorrelation stretch technique is significantly performed better than Wiener filtering based on the experimental results and independent sample T test results using IBM-SPSS software.

Efficient image enhancement using improved RIQMC based ROHIM model

Efficient image enhancement using improved RIQMC based ROHIM model

Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement.

Underwater images typically suffer from color deviations and low visibility due to the wavelength-dependent light absorption and scattering. To deal with these degradation issues, we propose an efficient and robust underwater image enhancement method, called MLLE. Specifically, we first locally adjust the color and details of an input image according to a minimum color loss principle and a maximum attenuation map-guided fusion strategy. Afterward, we employ the integral and squared integral maps to compute the mean and variance of local image blocks, which are used to adaptively adjust the contrast of the input image. Meanwhile, a color balance strategy is introduced to balance the color differences between channel a and channel b in the CIELAB color space. Our enhanced results are characterized by vivid color, improved contrast, and enhanced details. Extensive experiments on three underwater image enhancement datasets demonstrate that our method outperforms the state-of-the-art methods. Our method is also appealing in its fast processing speed within 1s for processing an image of size 1024×1024×3 on a single CPU. Experiments further suggest that our method can effectively improve the performance of underwater image segmentation, keypoint detection, and saliency detection. The project page is available at https://li-chongyi.github.io/proj_MMLE.html.

AUTOMATED POLYP DETECTION IN COLONOSCOPY VIDEOS USING IMAGE ENHANCEMENT AND SALIENCY DETECTION ALGORITHM

Colonoscopy has proven to be an active diagnostic tool that examines the lower half of the digestive system’s anomalies. This paper confers a Computer-Aided Detection (CAD) method for polyps from colonoscopy images that helps to diagnose the early stage of Colorectal Cancer (CRC). The proposed method consists primarily of image enhancement, followed by the creation of a saliency map, feature extraction using the Histogram of Oriented-Gradients (HOG) feature extractor, and classification using the Support Vector Machine (SVM). We present an efficient image enhancement algorithm for highlighting clinically significant features in colonoscopy images. The proposed enhancement approach can improve the overall contrast and brightness by minimizing the effects of inconsistent illumination conditions. Detailed experiments have been conducted using the publicly available colonoscopy databases CVC ClinicDB, CVC ColonDB and the ETIS Larib. The performance measures are found to be in terms of precision (91.69%), recall (81.53%), F1-score (86.31%) and F2-score (89.45%) for the CVC ColonDB database and precision (90.29%), recall (61.73%), F1-score (73.32%) and F2-score (82.64%) for the ETIS Larib database. Comparison with the futuristic method shows that the proposed approach surpasses the existing one in terms of precision, F1-score, and F2-score. The proposed enhancement with saliency-based selection significantly reduced the number of search windows, resulting in an efficient polyp detection algorithm.