Image Enhancement Techniques Research Articles

A lightweight feature activation guided multi-receptive field attention network for light compensation

Image enhancement techniques are commonly used to improve problems such as lack of brightness, high noise, and low contrast in low-light images. Notably, deep learning-based approaches have recently achieved substantial advancements in this domain. However, learning-based methods often require many parameters and multi-layer network structures to achieve high-quality enhancement effects, which limits their application in real-time image processing. To solve this problem, a lightweight Feature Activation Guided Multi-Receptive Field Attention Network (FAMANet) is designed in this paper. The Wavelet Feature Activation Block (WFAB) introduced in the network utilizes the discrete wavelet transform and residual connection to achieve selective activation of image features, thus reducing the redundant information in the feature map and improving the computational efficiency. In addition, the Multi-Receptive Field Attention (MRFA) introduced in this paper addresses the issue of inadequate pixel information and feature map loss stemming from a single input image by concentrating on the image structure, spanning from intricate details to the overall composition. By better-utilizing image information and distinguishing between global and local features, MRFA can improve the speed and efficiency of real-time image processing. After sufficient experimental validation, FAMANet significantly outperforms state-of-the-art methods in low-light image enhancement and exposure correction tasks.

Underwater Imagery Enhancement with Multi-Channel Histogram Equalization, Depth-Adaptive Correction, and Deep Reinforcement Learning

Underwater imagery plays a pivotal role in various scientific, industrial, and recreational applications, ranging from marine biology and oceanography to underwater archaeology and resource exploration. However, capturing high-quality images in underwater environments poses unique challenges due to factors such as light attenuation, color distortion, and particulate matter suspended in the water. In recent years, significant advancements have been made in the development of advanced techniques for enhancing underwater imagery aimed at improving perceptual quality, sharpness, and detail preservation. This research explores state-of-the-art enhancement techniques, focusing on multi-channel histogram equalization and depth-adaptive correction in the context of deep reinforcement learning (DRL) and variational autoencoders (VAE). The research presents a comprehensive analysis of the performance of these techniques using numerical metrics such as perceptual quality, sharpness, and detail preservation, derived from experimental evaluations. The findings demonstrate the effectiveness of VAE in enhancing perceptual quality and sharpness, with underwater image dehazing exhibiting commendable results as well. Furthermore, Convolutional Neural Networks (CNN) emerge as a promising approach for detail preservation in underwater images. By advancing the capabilities of underwater image enhancement techniques, this research contributes to the broader goal of unlocking the full potential of underwater exploration and understanding. The findings reveal significant improvements in image quality achieved by these techniques. VAE exhibits the highest perceptual quality scores, averaging around 0.84, with sharpness scores reaching approximately 0.91.

Literature Review on Digital Image Processing and Its Techniques

Purpose: Understand the current state of knowledge in the field of digital image processing. Identify key concepts, theories, and methodologies that have been explored in previous research. Explore the various techniques and algorithms used in digital image processing. Evaluate the strengths and weaknesses of existing methods. Identify gaps or limitations in current techniques. Provide a context for the research by summarizing and synthesizing relevant studies. Show how different studies contribute to the overall understanding of digital image processing. Identify gaps in the existing literature that your research can address. Determine areas where further investigation is needed. Offer a foundation for discussion and interpretation of your results in the context of existing literature. Enable you to relate your findings to the broader field of digital image processing. Design/Methodology/Approach: Clearly define the scope of your literature review (e.g., specific techniques, applications, or periods). State the objectives of the literature review, such as identifying trends, evaluating methodologies, or addressing research gaps. Develop a comprehensive search strategy to identify relevant literature. Utilize academic databases, journals, conferences, and other reputable sources. Use a combination of keywords, Boolean operators, and controlled vocabulary (e.g., MeSH terms) to refine search queries. Systematically review and select relevant literature based on the established criteria. Document the process, including databases searched, keywords used, and reasons for inclusion/exclusion. Identify gaps in the existing literature and propose potential avenues for future research. Discuss the implications of these gaps for the advancement of knowledge in the field Findings/Results: Literature often discusses various image enhancement techniques such as histogram equalization, contrast stretching, and spatial filtering. Researchers explore the effectiveness of these techniques in improving image quality for different applications. Segmentation methods, including thresholding, region-based segmentation, and clustering algorithms, are frequently discussed. Object recognition and classification techniques using features like texture, color, and shape are common topics. Different image compression algorithms, such as JPEG, JPEG2000, and various wavelet-based methods, are often compared in terms of compression ratio and quality. The literature might address real-time image processing challenges and solutions, especially in applications like video surveillance, autonomous vehicles, and augmented reality. Originality/Value: Ensure that the literature review comprehensively covers key and recent works in digital image processing. This includes foundational theories, algorithms, and applications. Identify seminal papers, landmark studies, and recent advancements to create a timeline of the field's development. Paper Type: Review of existing literature

Detection of dental periapical lesions using retinex based image enhancement and lightweight deep learning model

Dental periapical lesions, commonly associated with inflammation around the tooth apex, pose a significant challenge in early diagnosis and treatment. This study introduces a novel approach for the detection of dental periapical lesions through the integration of Retinex-based image enhancement techniques and a lightweight deep learning model. The Retinex algorithm is employed to enhance the radiographic images, addressing issues related to inconsistent illumination and contrast. Subsequently, a tailored lightweight deep learning model is designed to efficiently extract relevant features from the enhanced images. Present methodology leverages a dataset of dental radiographs to train and evaluate the deep learning model, incorporating a diverse range of periapical lesion cases. The model is optimized for computational efficiency while maintaining high accuracy, making it suitable for deployment in resource-constrained environment. To enhance precision in lesion detection, the U-Net segmentation technique has been incorporated, providing a sophisticated approach to delineate and analyze specific areas of interest within the radiographic images. This addition further refines our diagnostic framework, contributing to the robustness of lesion identification. Experimental results demonstrate the effectiveness of the Retinex-based image enhancement in improving the visibility of periapical lesions. The lightweight deep learning model exhibits promising performance in accurately detecting and classifying dental periapical lesions, showcasing its potential for early and efficient diagnosis. The results obtained from the present model are compared with those from Convolutional Neural Network (CNN) as well as with diagnosis of expert practitioners and the model is observed to perform very well. The study contributes to the advancement of computer-aided diagnostic tools in dentistry, offering a scalable and accessible solution for the identification of dental periapical lesions through the fusion of image enhancement and lightweight deep learning techniques.

A few-shot target detection method for wildfires: Research example of Fujian Province, China

Unmanned aerial vehicle (UAV) remote-sensing images have a wide range of applications in wildfire monitoring, providing invaluable data for early detection and effective management. This paper proposes an improved few-shot target detection algorithm tailored specifically for wildfire detection. The quality of UAV remote-sensing images is significantly improved by utilizing image enhancement techniques such as Gamma change and Wiener filter, thereby enhancing the accuracy of the detection model. Additionally, ConvNeXt-ECA is used to focus on valid information within the images, which is an improvement of ConvNeXt with the addition of the ECANet attention mechanism. Furthermore, multi-scale feature fusion is performed by adding a feature pyramid network (FPN) to optimize the extracted small target features. The experimental results demonstrate that the improved algorithm achieves a detection accuracy of 93.2%, surpassing Faster R-CNN by 6.6%. Moreover, the improved algorithm outperforms other target detection algorithms YOLOv8, RT-DETR, YoloX, and SSD by 3.4%, 6.4%, 7.6% and 21.1% respectively. This highlights its superior recognition accuracy and robustness in wildfire detection tasks.

Adaptive fuzzy weighted median filter for microcalcifications detection in digital breast tomosynthesis images

Breast cancer is a global leading cause of female mortality. Digital breast tomosynthesis (DBT) is pivotal for early breast cancer detection, with microcalcifications serving as crucial indicators. However, the movement of the DBT machine introduces blurry artefacts, potentially impacting accurate diagnosis. This study addresses this challenge by proposing an adaptive fuzzy weighted median filter (AFWMF) to enhance DBT images and aid microcalcification diagnosis. AFWMF automatically determines optimal parameters based on input images, outperforming conventional methods with a threshold range (C) from peak to end of switching. Quantitative assessment reveals peak signal to noise ratio (PSNR), and mean absolute error (MAE) values of 96.2267 and 0.0000636, respectively, demonstrating a significant improvement in microcalcification detection. This study contributes an effective and adaptive enhancement technique for DBT images, promising better breast cancer diagnosis, particularly in microcalcification scenarios.

Evaluating ASTER data and field spectrometry for lithological discrimination in semi-arid region, Northeast Kohat Plateau, Pakistan

This study employs data of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor to delineate and map the distribution of sedimentary lithologies in the semi-arid region of Kohat Plateau, Pakistan. False color composites (FCC) and various image transformation and enhancement techniques including the principal component analysis (PCA), minimum noise fraction (MNF), and band rationing (BR) were used successfully to differentiate four lithological classes. These lithologies include chemically/biochemically formed beds of the marine environment and detrital sequences of marginal marine to the riverine environment. FCC from original reflectance data, PCA, and BR techniques displayed more prominent lithological variation. To map the lithology and show the potential of ASTER data, field spectrometry over the barren lithologies was carried out. The end-member spectra from field spectrometry shows strong agreement with the pixels spectra from ASTER scene. The Spectral Angle Mapper (SAM) mapping method was then used to produce a classified lithological map, where the image pixels spectra proved more suitable reference, in comparison to the end-member spectra. The accuracy of the classified lithological map was evaluated based on field-based point data, which resulted an overall accuracy of 70% and a Kappa coefficient value of 0.679. Carbonates and evaporites showed relatively higher user and producer accuracies which are attributed to their topographic behavior and weathered scree over the adjacent rock unit. The final lithological map provided a clearer picture of surface geology where the existing geological maps lacked lithological continuity.

Enhancing assisted diagnostic accuracy in scalp psoriasis: A Multi-Network Fusion Object Detection Framework for dermoscopic pattern diagnosis.

Dermoscopy is a common method of scalp psoriasis diagnosis, and several artificial intelligence techniques have been used to assist dermoscopy in the diagnosis of nail fungus disease, the most commonly used being the convolutional neural network algorithm; however, convolutional neural networks are only the most basic algorithm, and the use of object detection algorithms to assist dermoscopy in the diagnosis of scalp psoriasis has not been reported. Establishment of a dermoscopic modality diagnostic framework for scalp psoriasis based on object detection technology and image enhancement to improve diagnostic efficiency and accuracy. We analyzed the dermoscopic patterns of scalp psoriasis diagnosed at 72nd Group army hospital of PLA from January 1, 2020 to December 31, 2021, and selected scalp seborrheic dermatitis as a control group. Based on dermoscopic images and major dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, we investigated a multi-network fusion object detection framework based on the object detection technique Faster R-CNN and the image enhancement technique contrast limited adaptive histogram equalization (CLAHE), for assisting in the diagnosis of scalp psoriasis and scalp seborrheic dermatitis, as well as to differentiate the major dermoscopic patterns of the two diseases. The diagnostic performance of the multi-network fusion object detection framework was compared with that between dermatologists. A total of 1876 dermoscopic images were collected, including 1218 for scalp psoriasis versus 658 for scalp seborrheic dermatitis. Based on these images, training and testing are performed using a multi-network fusion object detection framework. The results showed that the test accuracy, specificity, sensitivity, and Youden index for the diagnosis of scalp psoriasis was: 91.0%, 89.5%, 91.0%, and 0.805, and for the main dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, the diagnostic results were: 89.9%, 97.7%, 89.9%, and 0.876. Comparing the diagnostic results with those of five dermatologists, the fusion framework performs better than the dermatologists' diagnoses. Studies have shown some differences in dermoscopic patterns between scalp psoriasis and scalp seborrheic dermatitis. The proposed multi-network fusion object detection framework has higher diagnostic performance for scalp psoriasis than for dermatologists.

A proposed CLCOA Technique Based on CLAHE using Cat Optimized Algorithm for Plants Images Enhancement

Image Enhancement is one of the mainly significant with complex techniques in image study. The purpose of image enhancement is to advance the optical presence of an image, or to support a “improved convert representation for future mechanized image processing. Various images similar medical images, satellite images, natural with even real life photographs which have a lowly contrast and noise. This study presents a new enhancement technique based on standard contrast limited adaptive histogram equalization (CLAHE) technique for image enhancement which its name CLCOA. The suggested technique depends on augmentation of swarm intelligence via using Cat Swarm Optimization algorithm (CSO). The swarm intelligence is used to obtain the optimal structure of CLAHE technique. Tomato plant images have used and applied as dataset because of its important and influence in our life. For fair analysis of two techniques, Absolute Mean Brightness Error (AMBE), peak signal-to-noise ratio (PSNR), entropy and Contrast Gain of fundus images are analyzed by using MATLAB. The results show that performance of the proposed technique reveals the efficiently and robustness when compared results of standard technique.

Enhancing Rice Crop Resilience: Leveraging Image Processing Techniques in Deep Learning Models to Predict Salinity Stress of Rice during the Seedling Stage

One of the most significant staple crops in the world is rice. Rice seedlings are particularly susceptible to salt stress during the seedling stage, which can negatively affect crop quality and yield. Traditional approaches for assessing the susceptibility of rice crops to salt stress during the seedling stage are deemed inadequate and time consuming. The study emphasizes the necessity of employing a deep learning model instead of traditional methods to identify and classify salinity stress in rice seedlings using field images. To predict salinity stress in rice crops, this research examines the significance of image processing methods employed in deep learning models. To enhance the clarity and visual representation of salinity-induced stress symptoms, we explore several image enhancement techniques, such as noise reduction, contrast augmentation, and image normalization. To further capture and quantify the distinct visual features related to salinity stress, feature extraction techniques such as texture analysis, shape analysis, and color-based segmentation are used. We employ a deep learning model such as VGG16 and VGG19 models to use these extracted features as input to effectively classify the severity of salinity stress in rice seedlings as 1,3,5,7,9 scores. A comprehensive set of rice seedling images from field taken under various salinity stress conditions is used to assess the suggested method. The effectiveness of image processing techniques in improving the discriminatory power of deep learning models for salinity stress prediction is demonstrated by experimental results with 99.40%. The combination of image enhancement and feature extraction methods significantly improves the overall accuracy and reliability of the predictions, enabling farmers to make informed decisions regarding crop management and potential interventions to mitigate salinity stress.

Employing deep learning and transfer learning for accurate brain tumor detection

Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, and X-ray imaging in its effectiveness. Despite this, brain tumor diagnosis remains a challenging endeavour due to the intricate structure of the brain. This study delves into the potential of deep transfer learning architectures to elevate the accuracy of brain tumor diagnosis. Transfer learning is a machine learning technique that allows us to repurpose pre-trained models on new tasks. This can be particularly useful for medical imaging tasks, where labelled data is often scarce. Four distinct transfer learning architectures were assessed in this study: ResNet152, VGG19, DenseNet169, and MobileNetv3. The models were trained and validated on a dataset from benchmark database: Kaggle. Five-fold cross validation was adopted for training and testing. To enhance the balance of the dataset and improve the performance of the models, image enhancement techniques were applied to the data for the four categories: pituitary, normal, meningioma, and glioma. MobileNetv3 achieved the highest accuracy of 99.75%, significantly outperforming other existing methods. This demonstrates the potential of deep transfer learning architectures to revolutionize the field of brain tumor diagnosis.

Improving Digital Satellite Image for security purposes

Satellite imagery is employed in many different fields of study. These pictures have serious quality problems. Image enhancement algorithms, however, can improve it in terms of contrast, brightness, feature elimination from noise contents, etc. These algorithms present and analyse the picture's properties by sharpening, focusing, or smoothing the image. Therefore, the specific application determines the goal of picture enhancement. This paper briefly overviews picture enhancement methods that produce optimal and progressive outcomes for satellite images used for secured remote sensing. To do this, various image enhancement techniques are used, which are widely used today to improve image quality across various image processing applications. Some commonly used image enhancement techniques include spatial filtering, contrast stretching, and histogram equalisation. These techniques aim to enhance the visual quality of satellite images by adjusting brightness and contrast and reducing noise. These methods can also improve the interpretability of the images for remote sensing purposes. The enhancement of satellite images finds use in several fields, particularly security. It is essential for security applications, including threat detection, border control, and surveillance. Security professionals may more effectively analyse and understand data to spot any dangers or questionable activity by boosting the visual details and general quality. Keywords: Satellite image analysis; mean filter; secured application; SVM; wavelet transformation

A novel intuitionistic fuzzy generator for low-contrast color image enhancement technique

Fuzzy logic systems play a significant role in various fields including image processing. Addressing the challenge of enhancing low-illumination images, which often contain a high degree of uncertain information, is a complex task. To overcome this issue, this study introduces a novel intuitionistic fuzzy generator for enhancing low-contrast images. Initially, a low-illumination image is considered and the image is then fuzzified using a fuzzification technique. The fuzzified image is converted into a intuitionistic fuzzy image using the proposed novel intuitionistic fuzzy generator and then applied a contrast limited adaptive histogram equalization method to obtain the enhanced image. The obtained results are compared with the existing studies. With the outcome of the performance measures such as entropy, no-reference low-light image enhancement evaluation and contrast improvement index, it is perceived that the proposed technique yields a finer results than the previous studies.

Image Color Balance with Laplacian and Gaussian Pyramid (CBLGP) Algorithm

In recent times, technology has rapidly and significantly evolved across all sectors. Digital image processing stands out as a modern technology aimed at achieving clear images. However, digitized images often encounter issues of low quality, such as unclear or underwater images that require enhancement for better visibility. These problems stem from factors like deficient focusing, lighting, and various constraints leading to low contrast, shading, and artifacts. Underwater and satellite images consistently face less-than-ideal conditions due to environmental factors like light refraction in water, particle scattering, and dust in aquatic environments. Similarly, challenges in space, such as poor illumination and lack of contrast, further complicate image analysis. Overcoming these obstacles is crucial for extracting valuable information, necessitating advanced processing techniques. This paper introduces an enhanced Gaussian/Laplace color balance-fusion algorithm designed to improve image visibility. Modifications to certain equations result in sharper and clearer images. The algorithm begins by determining the white balance of the input RGB color image and subsequently enhances its intensity. Edge improvement is carried out separately using a depth filter. The weights for each image are then determined and combined to form a Laplace Pyramid. A color restoration technique is applied to process the resulting image, producing the final enhanced image. While existing methods for image contrast enhancement typically focus on image features, they often neglect user characteristics. This paper explores the application of image sharpening, a prominent image enhancement technique, in clearing underwater or low-quality images using the proposed algorithm. Keywords: EDSHE, High pass filter, White patch ratix, Laplacian pyramid, Color restoration.

IoT Based Smart Attendance Management System

Abstract: This research work has application for attendance system of employer's and students in general. The system will facilitate institutions! organization to make attendance individual in time along with data information thumb impression will be taken as a signature for the system entry. Main design and challenge in this system is the design of database architecture and its business logic. I. AIMS AND OBJECTIVE The aim of this system is to implement in C#.net set of reliable techniques for fingerprint image enhancement and minutiae extraction. The performance of these techniques will be evaluated on a fingerprint data set. In combination with these development techniques, statistical experiments can then be performed on the fingerprint data set. The results from these experiments can be used to help us better understand what is involved in determining the statistical uniqueness of fingerprint minutiae. The main aim that this system would test whether attendance by fingerprint is enough for identification. It is expected that the work in this system will reach the stage of being able to fully test hypothesis. II. BACKGROUND/CONTEXT Fingerprints are the oldest form of biometric identification. Modem fingerprint-based identification is used in forensic science, and in biometric systems such as civilian identification devices. Keywords: the fingerprint data, statistical uniqueness of fingerprint minutiae.

Hybrid technique for fundus image enhancement using modified morphological filter and denoising net

Hybrid technique for fundus image enhancement using modified morphological filter and denoising net

An OpenMP-based parallel implementation of image enhancement technique for dark images

An OpenMP-based parallel implementation of image enhancement technique for dark images

Lithological discrimination of the Fawakhir-Atalla belt in the Central Eastern Desert of Egypt based on Landsat-9 remote sensing data, airborne gamma-ray spectrometry, field and petrographic investigations with implications on the evolution of the Arabian Nubian Shield

The Central Eastern Desert (CED) is a key territory for understanding the evolution of the Arabian Nubian Shield (ANS) in Egypt as it contains Cryogenian-Tonian ensimatic supracrustal assemblages, such as ophiolitic and arc volcanic rocks, as well as gold deposits. So, it has been the subject of extensive studies over the last two decades. However, the lithological mapping of the Fawakhir-Atalla belt, located west of the Meatiq gneiss dome (infrastructure) of the CED, is still debated, as the magmatic texture of some rocks was partially obscured owing to the shearing and alteration related to the Najd Fault System, mantle CO2-bearing fluid, and igneous activity in the final stage of the Pan-African Orogeny. In this regard, integrated studies of airborne gamma-ray spectrometry and Landsat-9 satellite imagery processing techniques, as well as field and petrographic investigations, have been used for the lithological mapping of the belt. The results led to the following sequence; ophiolitic rocks > island arc assemblages > older granitoids (syn-tectonic subduction-related) > Hammamat Group (molasse-type origin) > post-Hammamat felsite > younger granite (post-tectonic suture-related). The applied color composite images, selective band ratios, and image transformation techniques show a clear distinction between rock units. Older granitoids are tonalite to quartz diorite, while younger granites are monzo-to syenogranite. They are well-discriminated from each other using band ratios, and younger granites have the highest K, eU, and eTh values coherent with the post-Hammamat felsite. The image enhancement and image transformation techniques show a clear discrepancy between the mélange rocks and Hammamat conglomerates. Mélange rocks are composed of fragments embedded in a foliated matrix, while Hammamat conglomerates are grain-supported with pebbles of volcanic source and have higher K, eU, and eTh values. Volcanic rocks in the belt are not a part of the Dokhan series as they are metamorphosed with a composition range from meta-basalts to meta-rhyolite, and show mainly aphanitic to infrequently porphyritic textures, which are typical of island arc metavolcanics.

Research on image deformation monitoring algorithm based on binocular vision

Monitoring the deformation of critical structures such as tunnel、buildings and bridges is critical to ensure the safety of infrastructures. Non-contact deformation monitoring technology based on digital imagery has emerged as a prevalent and effective monitoring method, which enables cost-effective and efficient tracking of structural deformations. This paper aims to develop an automated deformation monitoring methodology using the binocular vision. First, standard preprocessing of digital images is performed through the integration of filtering algorithms and image enhancement techniques. Furthermore, by combining the Scale Invariant Feature Transform (SIFT) feature matching algorithm, the inverse compositional gauss–newton iterative algorithm, and interpolation methodologies, the planar displacement of the monitored targets can be calculated precisely, with sub-pixel precision being achieved. Subsequently, by adopting an enhanced Semi-Global Matching (SGM) algorithm, stereo matching of binocular images were accomplished. This process yields a disparity map, from which the depth map is computed. Then, precise measurement of the vertical displacement of the monitoring target can be obtained. This enables accurate measurement of three-dimensional displacements of the monitored targets. Finally, the developed methodology undergoes a comparative assessment against traditional measurement methods and existing algorithms to validate its measurement accuracy. The results prove that the deformation measurement technique based on binocular vision proposed in this paper captures structural deformation effectively. The accuracy surpasses that of existing measurement algorithms, and its efficiency outperforms traditional measurement methods.

Multi-scale fusion for image enhancement in shield tunneling: a combined MSRCR and CLAHE approach

Aiming at addressing the complex lighting conditions in shield construction environments and enhancing the contrast of low-light images while preserving image details, this paper presents an image enhancement technique based on multi-scale fusion. The proposed method first duplicates the input image into two copies. Subsequently, the first copy undergoes color and contrast adjustment via the adaptive weighted Retinex algorithm for color restoration (A-MSRCR) algorithm. Meanwhile, the second copy undergoes contrast enhancement and detail preservation by combining Gamma correction and Contrast Limited Adaptive Histogram Equalization (CLAHE). Finally, a normalized weight map is introduced to facilitate multi-scale fusion of the two enhanced images, ensuring an overall enhancement in both contrast and detail. The experimental results show that the proposed method provides better visual quality and objective data than the compared methods. Moreover, it offers valuable support for subsequent recognition and localization tasks.