Enhancement Algorithm Research Articles

EXPLORING AI-BASED TECHNIQUES FOR IMAGE PROCESSING USING STREAMLIT APPLICATION

Abstract: In this paper, we delve into the realm of image processing using AI-based techniques, with a focus on leveraging the Streamlit application for development and deployment. Our exploration encompasses a wide array of methodologies, ranging from traditional image enhancement algorithms to cutting-edge deep learning architectures. Through a series of experiments, we demonstrate the efficacy of AI-based approaches in addressing various challenges in image processing, including denoising, super- resolution, segmentation, and classification. The scope of our experiments extends to diverse domains, including medical imaging, remote sensing, and digital photography. Significant data obtained from benchmark datasets and real-world scenarios serve as the foundation for our analyses. Our findings underscore the transformative potential of AI-driven image processing techniques, showcasing their ability to enhance image quality, extract meaningful information, and facilitate decision-making processes. Furthermore, we present insights into the practical implications of deploying Streamlit applications for rapid prototyping and deployment of image processing solutions. Through comprehensive evaluations and comparisons with existing methodologies, we elucidate the strengths and limitations of AI-based techniques, paving the way for future advancements in this burgeoning field. IndexTerms – Python, Machine Learning, Deep Learning, Open CV, Matplotlib.

Feature selection for multi-labeled data based on label enhancement technique and mutual information

In multi-label data, the importance of each label within the logical label vector varies for each sample, and there exist inherent correlations among the labels. However, the logical label vector fails to capture these nuances. Consequently, relying solely on this vector for feature selection in multi-label data results in underutilization of supervisory information. To address this issue, this paper introduces a novel label enhancement algorithm. This algorithm leverages neighborhood information derived from features to transform the logical label vector into a label distribution that effectively reflects label differences and correlations. Subsequently, we propose a feature selection algorithm tailored for multi-label data, which incorporates both the transformed label distribution and mutual information. This algorithm not only accounts for the mutual information between features and label distributions but also captures the mutual information among features themselves. Finally, we evaluate our proposed feature selection algorithm against five state-of-the-art multi-label feature selection algorithms on ten publicly available datasets. The experimental results reveal that our algorithm outperforms its competitors in six distinct evaluation metrics, achieving an average performance improvement of approximately 9%. This substantial enhancement underscores the efficacy of our algorithm in handling complex multi-label data.

Bit depth enhancement method based on visual contrast perception features

High-bit images can show more detail than low-bit images do. False contours in the low-bit images should be suppressed when converting low-bit images to high-bit ones. In this paper, a contrast-aware bit depth enhancement (BDE) module is proposed based on the visual perceptual feature which models the relationship of contrast sensitivity and image bit depth. And a bit depth enhancement network is constructed by the cascade of this module. The experimental results show that the bit depth enhancement algorithm based on this network structure has the best or second-best peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics when compared with the related algorithms, and on average, our PSNR (40.20 dB) is only 1.03% and SSIM (0.9681) is only 0.11% lower than that of the state-of-the-arts, but uses 6.7% fewer parameters (11.2M). And visual comparisons show that our algorithm can effectively suppress false contours and color distortions, resulting in high-bit images with better quality.

Single-Shot Direct Transmission Terahertz Imaging Based on Intense Broadband Terahertz Radiation.

There are numerous applications of terahertz (THz) imaging in many fields. However, current THz imaging is generally based on scanning technique due to the limited intensity of the THz sources. Thus, it takes a long time to obtain a frame image of the target and cannot meet the requirement of fast THz imaging. Here, we demonstrate a single-shot direct THz imaging strategy based on a broadband intense THz source with a frequency range of 0.1~23 THz and a THz camera with a frequency response range of 1~7 THz. This THz source was generated from the laser-plasma interaction, with its central frequency at ~12 THz. The frame rate of this imaging system was 8.5 frames per second. The imaging resolution reached 146.2 μm. With this imaging system, a single-shot THz image for a target object with a size of more than 7 cm was routinely obtained, showing a potential application for fast THz imaging. Furthermore, we proposed and tested an image enhancement algorithm based on an improved dark channel prior (DCP) theory and multi-scale retinex (MSR) theory to optimize the image brightness, contrast, entropy and peak signal-to-noise ratio (PSNR).

SFMnet: Edge detection of HABs based on spatial feature mapping encoder-decoder network

Achieving high-accuracy real-time recognition of marine red tide algae images is crucial in implementing red tide algae early warning systems. However, due to uneven lighting and the deposition of unknown suspended particles in seawater, real-time collected harmful algal blooms (HABs) microscopic images in marine engineering suffer from poor clarity and blurred cell contours, leading to lower accuracy of current edge detection models for microscopic images. Firstly, to enhance the quality of degraded images in the original HABs dataset, we design an image enhancement algorithm based on underwater image restoration models, which involves precise depth estimation of seawater scenes and fusion of transmission maps. This algorithm effectively eliminates chromatic aberration and enhances image edge details and contrast. Secondly, we propose a deep-learning model based on the VGG16 network. This model integrates multi-scale features through the Spatial Feature Mapping module to improve edge detection accuracy. Experimental results using real-time HABs microscopic image data show that the proposed enhancement method achieves the best results regarding contrast, image enhancement metrics, and clarity gradient. Moreover, the edge detection method achieves an ODS of 0.636 and an OIS of 0.701. The results demonstrate that performing edge detection after the image enhancement preprocess significantly improves detection accuracy.

Innovative cloud quantification: deep learning classification and finite-sector clustering for ground-based all-sky imaging

Abstract. Accurate cloud quantification is essential in climate change research. In this work, we construct an automated computer vision framework by synergistically incorporating deep neural networks and finite-sector clustering to achieve robust whole-sky image-based cloud classification, adaptive segmentation and recognition under intricate illumination dynamics. A bespoke YOLOv8 (You Only Look Once 8) architecture attains over 95 % categorical precision across four archetypal cloud varieties curated from extensive annual observations (2020) at a Tibetan highland station. Tailor-made segmentation strategies adapted to distinct cloud configurations, allied with illumination-invariant image enhancement algorithms, effectively eliminate solar interference and substantially boost quantitative performance even in illumination-adverse analysis scenarios. Compared with the traditional threshold analysis method, the cloud quantification accuracy calculated within the framework of this paper is significantly improved. Collectively, the methodological innovations provide an advanced solution to markedly escalate cloud quantification precision levels imperative for climate change research while offering a paradigm for cloud analytics transferable to various meteorological stations.

IT Parkinson’s Disease Diagnostics Based on the Freezing of Gait Analysis Using Long Short Term Memory Neural Network

An analysis of methods for processing data from gait deceleration sensors for detecting Parkinson’s disease and a description of the development of a Parkinson’s recognition system based on neural networks with long short term memory (LSTM) are performed. The data used was a publicly available dataset of gait deceleration scores of patients with Parkinson’s disease, obtained using three wearable sensors to collect data from different parts of the body. The research was carried out using machine learning using an LSTM neural network. First, the DAPHNet datasets were segmented using a fixed sliding window algorithm. The wavelet algorithm was then used to extract features from the data set: wavelet entropy and energy, wavelet waveform length, variance and standard deviation of wavelet coefficient. Next, a data enhancement algorithm was used to balance the number of samples in the data sets. To train the model, an LSTM neural network was built with a six-layer network structure: input layer, LSTM layer, reLU layer, fully connected layer, Softmax layer and output layer. After training the model for 1000 iterations, the LSTM neural network algorithm achieved 96.3 % accuracy, 96.05 % precision, 96.5 % sensitivity, and 96.24 % average F1 score for recognizing Parkinson’s disease based on test datasets. Similar studies conducted by other scientific organizations achieved a maximum accuracy of 91.9 % for the same data sets.

SpaceLight: A Framework for Enhanced On-Orbit Navigation Imagery

In the domain of space rendezvous and docking, visual navigation plays a crucial role. However, practical applications frequently encounter issues with poor image quality. Factors such as lighting uncertainties, spacecraft motion, uneven illumination, and excessively dark environments collectively pose significant challenges, rendering recognition and measurement tasks during visual navigation nearly infeasible. The existing image enhancement methods, while visually appealing, compromise the authenticity of the original images. In the specific context of visual navigation, space image enhancement’s primary aim is the faithful restoration of the spacecraft’s mechanical structure with high-quality outcomes. To address these issues, our study introduces, for the first time, a dedicated unsupervised framework named SpaceLight for enhancing on-orbit navigation images. The framework integrates a spacecraft semantic parsing network, utilizing it to generate attention maps that pinpoint structural elements of spacecraft in poorly illuminated regions for subsequent enhancement. To more effectively recover fine structural details within these dark areas, we propose the definition of a global structure loss and the incorporation of a pre-enhancement module. The proposed SpaceLight framework adeptly restores structural details in extremely dark areas while distinguishing spacecraft structures from the deep-space background, demonstrating practical viability when applied to visual navigation. This paper is grounded in space on-orbit servicing engineering projects, aiming to address visual navigation practical issues. It pioneers the utilization of authentic on-orbit navigation images in the research, resulting in highly promising and unprecedented outcomes. Comprehensive experiments demonstrate SpaceLight’s superiority over state-of-the-art low-light enhancement algorithms, facilitating enhanced on-orbit navigation image quality. This advancement offers robust support for subsequent visual navigation.

Enhanced Power Law Transformation for Histopathology Images of Breast Cancer

Different image enhancement techniques are applied to improve the visual quality of an image on a display device. Contrast stretching, intensity level slicing with and without background, histogram equalization, logarithmic transformation and power law transformation are some image enhancement techniques. Most of the research work focuses on adaptive gamma correcting factors for better visualization of extremely low contrast images, giving less importance to the constant for enhanced visualization. This research proposes an efficient and less complex enhanced power law transformation (EPLT) approach to improve the contrast of dimmed and extremely bright images. The approach is a quick way to compute the value of C, i.e. constant for enhanced visualization. For better picture quality, it is very important to determine C automatically and the gamma correcting factor. This technique offers a novel and unique perspective on image contrast manipulation. The proposed enhancement technique is experimented on histopathology images of breast cancer, bright images and extremely dark images. The average peak signal-to-noise ratio (PSNR) for clinical data and BreakHis dataset is high for the proposed method are 16.52487 and 17.69335 respectively. The average RMSE for clinical data and BreakHis dataset is low for the proposed method are 40.88251 and 44.2546 respectively. It is observed that the proposed method yields the most satisfactory contrast enhancements based on performance comparison with other state-of-art enhancement algorithms and works efficiently on all types of images.

Elicitation of fetal ECG from abdominal recordings using Blind Source Separation techniques and Robust Set Membership Affine Projection algorithm for signal quality enhancement

Background:The utilization of non-invasive techniques for fetal cardiac health surveillance is pivotal in evaluating fetal well-being throughout the gestational period. This process requires clean and interpretable fetal Electrocardiogram (fECG) signals. Method:The proposed work is the novel framework for the elicitation of fECG signals from abdominal ECG (aECG) recordings of the pregnant mother. The comprehensive approach encompasses pre-processing of the raw ECG signal, Blind Source Separation techniques (BSS), Decomposition techniques like Empirical Mode Decomposition (EMD), and its variants like Ensemble Empirical Mode Decomposition (EEMD), and Complete Ensemble Empirical Mode Decomposition with Additive Noise (CEEMDAN). The Robust Set Membership Affine Projection (RSMAP) Algorithm is deployed for the enhancement of the obtained fECG signal. Result:The results show significant improvements in the elicited fECG signal with a maximum Signal Noise Ratio (SNR) of 31.72 dB and correlation coefficient = 0.899, Maximum Heart Rate(MHR) obtained in the range of 108–142 bpm for all the records of abdominal ECG signals. The statistical test gave a p-value of 0.21 accepting the null hypothesis. The Abdominal and Direct Fetal Electrocardiogram Database (ABDFECGDB) from PhysioNet has been used for this analysis. Conclusion:The proposed framework demonstrates a robust and effective method for the elicitation and enhancement of fECG signals from the abdominal recordings.

Retinex-based underwater image enhancement via adaptive color correction and hierarchical U-shape transformer

Underwater images can suffer from visibility and quality degradation due to the attenuation of propagated light and other factors unique to the underwater setting. While Retinex-based approaches have shown to be effective in enhancing the underwater image quality, the use of hand-crafted priors and optimization-driven solutions often prevent the adaptivity of these methods to different types of underwater images. Moreover, the commonly-used white balance strategy which often appears in the preprocessing stage of the underwater image enhancement (UIE) algorithms may give rise to unwanted color distortions due to the fact that wavelength-dependent light absorption is not taken into account. To overcome these potential limitations, in this paper, we present an effective UIE model based on adaptive color correction and data-driven Retinex decomposition. Specifically, an adaptive color balance approach which takes into account different attenuation levels for light with different wavelengths is proposed to adaptively enhance the three color channels. Furthermore, deep neural networks are employed for the Retinex decomposition, formulating the optimization problem as an implicit-prior-regularized model which is solved by learning the priors from a large training dataset. Finally, a hierarchical U-shape Transformer network which uses hierarchically-structured multi-scale feature extraction and selective feature aggregation is applied to the decomposed images for contrast enhancement and blur reduction. Experimental results tested on six benchmark underwater image datasets demonstrate the effectiveness of the proposed UIE model.

Machine Learning Algorithms for Enhancement of a Dual Square FSS for Sub 6G Applications

Frequency Selective Surfaces (FSS) play a very critical role now-a-days in electromagnetic engineering, which enables precise control of electromagnetic wave propagation for various applications. This conference article explores the integration of Machine Learning (ML) algorithms to optimization of the structure to achieve enhanced performance and efficiency for Sub 6G applications (6 GHz and 2.4 GHz). The Double Square Frequency Selective Surface (DS-FSS) consists of two distinct layers of square shaped periodic patterns. This configuration pro- vides greater flexibility in tailoring the overall frequency response compared to single layer FSS. A comprehensive data set has been prepared by simulating the electromagnetic responses from the Equivalent Circuit Model (ECM) which are fed to the ML algorithm to extract the features and build the regression model and to estimate the optimal parameters. The ML algorithms used are Linear Regression and Random Forest. These are then validated against the electromagnetic simulations using HFSS Software. Key words: Equivalent Circuit Model (ECM), Double Square Frequency Selective Surface (DS-FSS), Linear Regression, Random Forest.

LEFB: A new low-light image contrast enhancement algorithm

Low-light images are challenging for both human observation and computer vision algorithms due to low visibility. To address this issue, various image enhancement techniques such as dehazing, histogram equalization, and neural network-based methods have been proposed. However, most existing methods often suffer from the problems of insufficient contrast and over-enhancement while enhancing the brightness, which not only affects the visual quality of images but also adversely impacts their subsequent analysis and processing. To tackle these problems, this paper proposes a low-light image enhancement method called LEFB. Specifically, the low-light image is first transformed into the LAB color space, and the L channel controlling brightness is enhanced using a local contrast enhancement algorithm. Then, the enhanced image is further enhanced using an exposure fusion-based contrast enhancement algorithm, and finally, a bilateral filtering function is applied to reduce image edge blurriness. Experimental evaluations are conducted on real datasets with four comparison algorithms. The results demonstrate that the proposed method has superior performance in enhancing low-light images, effectively addressing problems of insufficient contrast and over-enhancement, while preserving fine details and texture information, resulting in more natural and realistic enhanced images.

Research on Unsupervised Low-Light Railway Fastener Image Enhancement Method Based on Contrastive Learning GAN

The railway fastener, as a crucial component of railway tracks, directly influences the safety and stability of a railway system. However, in practical operation, fasteners are often in low-light conditions, such as at nighttime or within tunnels, posing significant challenges to defect detection equipment and limiting its effectiveness in real-world scenarios. To address this issue, this study proposes an unsupervised low-light image enhancement algorithm, CES-GAN, which achieves the model’s generalization and adaptability under different environmental conditions. The CES-GAN network architecture adopts a U-Net model with five layers of downsampling and upsampling structures as the generator, incorporating both global and local discriminators to help the generator to preserve image details and textures during the reconstruction process, thus enhancing the realism and intricacy of the enhanced images. The combination of the feature-consistency loss, contrastive learning loss, and illumination loss functions in the generator structure, along with the discriminator loss function in the discriminator structure, collectively promotes the clarity, realism, and illumination consistency of the images, thereby improving the quality and usability of low-light images. Through the CES-GAN algorithm, this study provides reliable visual support for railway construction sites and ensures the stable operation and accurate operation of fastener identification equipment in complex environments.

Zero-shot denoising of microscopy images recorded at high-resolution limits.

Conventional and electron microscopy visualize structures in the micrometer to nanometer range, and such visualizations contribute decisively to our understanding of biological processes. Due to different factors in recording processes, microscopy images are subject to noise. Especially at their respective resolution limits, a high degree of noise can negatively effect both image interpretation by experts and further automated processing. However, the deteriorating effects of strong noise can be alleviated to a large extend by image enhancement algorithms. Because of the inherent high noise, a requirement for such algorithms is their applicability directly to noisy images or, in the extreme case, to just a single noisy image without a priori noise level information (referred to as blind zero-shot setting). This work investigates blind zero-shot algorithms for microscopy image denoising. The strategies for denoising applied by the investigated approaches include: filtering methods, recent feed-forward neural networks which were amended to be trainable on noisy images, and recent probabilistic generative models. As datasets we consider transmission electron microscopy images including images of SARS-CoV-2 viruses and fluorescence microscopy images. A natural goal of denoising algorithms is to simultaneously reduce noise while preserving the original image features, e.g., the sharpness of structures. However, in practice, a tradeoff between both aspects often has to be found. Our performance evaluations, therefore, focus not only on noise removal but set noise removal in relation to a metric which is instructive about sharpness. For all considered approaches, we numerically investigate their performance, report their denoising/sharpness tradeoff on different images, and discuss future developments. We observe that, depending on the data, the different algorithms can provide significant advantages or disadvantages in terms of their noise removal vs. sharpness preservation capabilities, which may be very relevant for different virological applications, e.g., virological analysis or image segmentation.

Thermal Infrared-Image-Enhancement Algorithm Based on Multi-Scale Guided Filtering

Obtaining thermal infrared images with prominent details, high contrast, and minimal background noise has always been a focal point of infrared technology research. To address issues such as the blurriness of details and low contrast in thermal infrared images, an enhancement algorithm for thermal infrared images based on multi-scale guided filtering is proposed. This algorithm fully leverages the excellent edge-preserving characteristics of guided filtering and the multi-scale nature of the edge details in thermal infrared images. It uses multi-scale guided filtering to decompose each thermal infrared image into multiple scales of detail layers and a base layer. Then, CLAHE is employed to compress the grayscale and enhance the contrast of the base layer image. Then, detail-enhancement processing of the multi-scale detail layers is performed. Finally, the base layer and the multi-scale detail layers are linearly fused to obtain an enhanced thermal infrared image. Our experimental results indicate that, compared to other methods, the proposed method can effectively enhance image contrast and enrich image details, and has higher image quality and stronger scene adaptability.

Underwater Image Enhancement Algorithm Based on Adversarial Training

Ocean observation is the first step in the development of the ocean, whose abundant resources and strategic significance are attracting increasing attention. Observation methods based on visual sensor networks have received great attention from researchers due to their visualization capability and high information capacity. However, below the sea surface, objective factors such as blurriness, turbulence, and underwater color casting can cause image distortion and affect the acquisition of images. In this paper, the enhancement of underwater images is tackled using an adversarial learning-based approach. First, pre-processing is applied to address the significant color casting in the dataset, thus enhancing feature learning for subsequent style transfer. Then, corresponding improvements are made to a generative adversarial network’s structure and loss functions to better restore the features of the network output. Finally, evaluations and comparisons are performed using underwater image quality assessment metrics and several public datasets. Through multidimensional experiments, the proposed algorithm is shown to exhibit excellent performance in both subjective and objective evaluation metrics compared to state-of-the-art algorithms, as well as in practical visual applications.

A Positive Sample Enhancement Algorithm with Fuzzy Nearest Neighbor Hybridization for Imbalance Data

A Positive Sample Enhancement Algorithm with Fuzzy Nearest Neighbor Hybridization for Imbalance Data

CMRight: Chinese Morph Resolution based on end-to-end model combined with enhancement algorithms

As the primary means of daily communication, SMS and instant messaging have the advantage of being inexpensive, but as a result, they have received the attention of practitioners in the underground industry. By using morph to bypass the regulation of platforms that mainly rely on rules, they disseminate information that draws traffic for industries such as pornography and gambling. To alleviate the above problems, existing researchers try to use the Chinese spelling error correction model, but the effect could be better. To this end, we manually analyze 11,358 deformed texts in real scenarios and identify four prevalent morph types in the field. Further, we propose a machine translation model-based algorithm for morph resolution, which achieves more than 96.2% BLEU and F1 values in the validation set. We also utilize the alignment and enhancement algorithms to improve the model performance by nearly 2.3% to 98.6%. The above results show that our proposed method can effectively identify the connection between the lexeme and the target word, thus resolving morphs and providing more efficient data support for traceability and information mining in the underground industry.

GAN-Based Super Resolution Algorithm for High-Quality Image Enhancement

Abstract: Super Resolution: Leveraging investigates the trans-formative potential of Generative Adversarial Networks (GANs) in the context of super resolution, utilizing state-of-the-art algorithms to achieve high-quality image enhancement. The study encompasses a comprehensive analysis of GAN architectures suchas SRGAN, ESRGAN, and others, exploring their strengths, limitations, and practical applications. The proposed methodology delves into dataset selection, preprocessing techniques, and the integration of advanced algorithms within the GAN framework. It evaluates the quantitative and perceptual performance of these techniques, addressing challenges and proposing avenues for future research. The findings contribute to the evolving landscapeof GAN-based super resolution, offering insights for researchers and practitioners in advancing image enhancement technologies