Improved Space Object Detection Based on YOLO11

This paper presents a multiscale histogram excess-distribution strategy addressing the structural limitations (i.e., insufficient dark-region restoration, block artifacts, ringing effects, color distortion, and saturation loss) of contrast-limited adaptive histogram equalization (CLAHE) and retinex-based image-contrast enhancement techniques. This method adjusts the ratio between the uniform and weighted distribution of the histogram excess based on the average tile brightness. At the coarsest scale, excess pixels are redistributed to histogram bins initially occupied by pixels, maximizing detail restoration in dark areas. For medium and fine scales, the contrast enhancement strength is adjusted according to tile brightness to preserve local luminance transitions. Scale-specific lookup tables are bilinearly interpolated and merged at the pixel level. Background restoration corrects unnatural tone compression by referencing the original image, ensuring visual consistency. A ratio-based chroma adjustment and color-restoration function compensate for saturation degradation in retinex-based approaches. An asymmetric Gaussian offset correction preserves structural information and expands the global dynamic range. The experimental results demonstrate that this method enhances local and global contrast while preserving fine details in low light and high brightness. Compared with various existing methods, this method reproduces more natural color with superior image enhancement.

Iris recognition system is the most secured, and faster means of identification and authentication. However, iris recognition system suffers a setback from blurring, low contrast and illumination due to low quality image which compromises the accuracy of the system. The acceptance or rejection rates of verified user depend solely on the quality of the image. In many cases, iris recognition system with low image contrast could falsely accept or reject user. Therefore this paper adopts Histogram Equalization Technique to address the problem of False Rejection Rate (FRR) and False Acceptance Rate (FAR) by enhancing the contrast of the iris image. A histogram equalization technique enhances the image quality and neutralizes the low contrast of the image at normalization stage. The experimental result shows that Histogram Equalization Technique has reduced FRR and FAR compared to the existing techniques.

In the field of image processing, improving low-contrast images is essential for enhancing the visibility of features that may be hard to see due to slight intensity variations. This issue often arises with images captured in poor lighting or unfavorable conditions, which can impede tasks such as feature extraction, object recognition, and edge detection. This paper presents an algorithm that employs fuzzy techniques to enhance low-contrast images. The approach utilizes a Pythagorean fuzzy set, which is an advanced version of a fuzzy set. The Pythagorean fuzzy set, by integrating membership and non−membership degrees, offers a more precise representation of image uncertainty. The proposed method constructs an increasing function to calculate the degree of non−membership, thereby amplifying the effectiveness of the enhanced image. By integrating this method with contrast limited adaptive histogram equalization, alongside a color restoration technique and brightness-preserving dynamic histogram equalization, the brightness and natural appearance of the original low-contrast image are maintained. We compare our approach to other methods, including conventional histogram equalization, intuitionistic fuzzy generators, interval−valued intuitionistic fuzzy generators, interval−valued intuitionistic fuzzy with contrast limited adaptive histogram equalization, LightNet, and FlightNet. Experimental results indicate that our method surpasses the techniques currently utilized in terms of visibility and color accuracy. Performance evaluation is performed using metrics such as the absolute mean brightness error, contrast improvement index, and structural similarity index.

Medical image fusion based on enhanced three-layer image decomposition and Chameleon swarm algorithm

An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE

Captive environments trigger the propagation and multiplication of parasites among different reptile species, thus weakening their immune response and causing infections and diseases. Technological advances of convolutional neural networks have opened a new field for detecting and classifying diseases which have shown great potential to overcome the shortcomings of manual detection performed by experts. Therefore, we propose an approach to identify six captive reptiles parasitic agents (Ophionyssus natricis, Blastocystis sp, Oxiurdo egg, Rhytidoides similis, Strongyloides, Taenia) or the absence of such parasites from a microscope stool images dataset. Towards this end, we first use an image segmentation stage to detect the parasite within the image, which combines the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique, the OTSU binarization method, and morphological operations. Then, we carry out a classification stage through MobileNet CNN under a transfer learning scheme. This method was validated on a stool image dataset containing 3616 images data samples and 26 videos from the six parasites mentioned above. The results obtained indicate that our transfer learning-based approach can learn a helpful representation from the dataset. We obtained an average accuracy of 94.26% across the seven classes (i.e., six parasitic agents and the absence of parasites), which statistically outperformed, at a 95% confidence level, a custom CNN trained from scratch.

Technique for preprocessing of digital mammogram

Human skin type classification using image processing and deep learning approaches

This research paper aims to propose a sophisticated AI technique namely Deep Neural Network (DNN) and Support Vector Machine (SVM) to extract and detect brain tumors. The preternatural growth in brain leading towards brain tumor requires proper assessment and timely recognition for further diagnosis. Magnetic Resonance Imaging (MRI) is effectively significant to achieve accuracy and efficiency in the overall treatment. The dimensions of MRI Images are resized as required for AlexNet input images that are further utilized in the processing stage and it is evident from MRI scans available in the manuscript. The approach used for the extraction of features is based on the application of image enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE) technique . The process is started by the initial processing of MRI images and application of grayscale in the preprocessing stage. The data input is eventually differentiated by adding extra commotion to the picture using median and morphology filter. The features after being converted into grayscale are extracted from this high-quality refined image with the aid of the AlexNet model and these are then moved to SVM and k-Nearest Neighbor (KNN) for identification of tumor. Finally, an exact position of the tumor is obtained and therefore it becomes easier to diagnose the type and remove the tumor.

For the phenomenon of low image contrast usually appearing in scanning electron microscope images, a low contrast microscopic image enhancement algorithm based on multi-technology fusion was proposed, in which the high frequency and low frequency components of images are separated to avoid the increased image noise, while the images was strengthened. Furthermore, the low contrast microscopic image enhancement was realized combining with Sobel operator, LoG operator and improved contrast limited adaptive histogram equalization method. Experimental results show that the proposed method highlights the image edges and details effectively, which can expand the dynamic grayscale range of image, increase the image contrast and entropy so lay the foundation for the microscopic image analysis and identification.

In the context of intelligent manufacturing for furniture, the classification of solid wood floors is critical for automating quality control and enhancing production efficiency. The final aesthetic of installed solid wood floors largely depends on the uniformity of color and texture. However, most current research does not address the simultaneous classification of both texture and color in solid wood floors, and there is a notable gap in studies focusing on the classification of color-difference wood floors and white-edge wood floors. This paper proposes a re-parameterized CNN-Transformer hybrid architecture based on the MobileViT model, named Rep-MobileViT. Specifically, the RepAIRB module is introduced, incorporating an asymmetric convolutional block (ACB) and a re-parameterized structure within the inverted residual block (IRB) module to enhance the network’s receptive field without increasing computational costs. Additionally, the RepMFF-MobileViT modu le is designed, utilizing a multi-feature fusion strategy and the RepTransformer structure to reduce the number of parameters while improving the network’s expressiveness and stability. In the preprocessing stage, a contrast-limited adaptive histogram equalization and brightness blending (CLAHE-LB) technique is employed to enhance image clarity, highlighting texture details while preserving original color information. Experimental results indicate that the proposed method achieves an overall classification accuracy of 94.98%, with a processing time of only 20.94 ms. Comparative analyses demonstrate that this approach outperforms existing machine learning methods by over 12% in classification accuracy.

Chest radiography is a common diagnostic imaging test, which contains an enormous amount of information about a patient. However, its interpretation is highly challenging. The accuracy of the diagnostic process is greatly influenced by image processing algorithms; hence enhancement of the images is indispensable in order to improve visibility of the details. This paper aims at improving radiograph parameters such as contrast, sharpness, noise level, and brightness to enhance chest radiographs, making use of a triangulation method. Here, contrast limited adaptive histogram equalization technique and noise suppression are simultaneously performed in wavelet domain in a new scheme, followed by morphological top-hat and bottom-hat filtering. A unique implementation of morphological filters allows for adjustment of the image brightness and significant enhancement of the contrast. The proposed method is tested on chest radiographs from Japanese Society of Radiological Technology database. The results are compared with conventional enhancement techniques such as histogram equalization, contrast limited adaptive histogram equalization, Retinex, and some recently proposed methods to show its strengths. The experimental results reveal that the proposed method can remarkably improve the image contrast while keeping the sensitive chest tissue information so that radiologists might have a more precise interpretation.

With the development of neural networks, object detection based on deep learning is developing rapidly, and its applications are gradually increasing. In the tire industry, detecting speckle interference bubble defects of tire crown has difficulties such as low image contrast, small object scale, and large internal differences of defects, which affect the detection precision. To solve these problems, we propose a new feature pyramid network based on Faster RCNN-FPN. It can fuse features across levels and directions to improve small object detection and localization, and increase object detection precision. The method has proven its effectiveness through cross-validation experiments. On a tire crown bubble defect dataset, the mAP [0.5:0.95] increased by 2.08% and the AP0.5 increased by 2.4% over the original network. The results show that the improved network significantly improves detecting tire crown bubble defects.

Face recognition is used to identify one or more persons from still images or a video image sequence of a scene by comparing input images with faces stored in a database. The face images used for matching the image in the database has to be of good quality with normal lighting condition and contrast. However, face images of poor illumination or low contrast could not be recognized properly. The objective of the work is to enhance the facial features eyes, nose, and mouth for poor contrast facial images for face recognition. The image enhancement is done by first detecting the face part, then applying contrast-limited adaptive histogram equalization technique and thresholding to enhance the facial features. The brightness of the facial features is enhanced by using logarithm transformation. The proposed image enhancement method is implemented on AR database, and the face images appear visually good when compared to original image. The effectiveness of the enhancement method is compared by analyzing the histogram.

The precise identification of hydrocarbon-rich zones is crucial for optimizing exploration and production processes in the oil industry. Magnetotelluric (MT) surveys play a fundamental role in mapping subsurface geological structures. This study presents a novel methodology for automatically delineating resistivity contrasts in MT models by employing advanced machine learning and computer vision techniques. This approach commences with data augmentation to enhance the diversity and volume of resistivity data. Subsequently, a bilateral filter was applied to reduce noise while preserving edge details within the resistivity images. To further improve image contrast and highlight significant resistivity variations, contrast-limited adaptive histogram equalization (CLAHE) was employed. Finally, k-means clustering was utilized to segment the resistivity data into distinct groups based on resistivity values, enabling the identification of color features in different centroids. This facilitated the detection of regions with significant resistivity contrasts in the reservoir. From the clustered images, color masks were generated to visually differentiate the groups and calculate the area and proportion of each group within the pictures. Key features extracted from resistivity profiles were used to train unsupervised learning models capable of generalizing across different geological settings. The proposed methodology improves the accuracy of detecting zones with oil potential and offers scalable applicability to different datasets with minimal retraining, applicable to different subsurface environments. Ultimately, this study seeks to improve the efficiency of petroleum exploration by providing a high-precision automated framework with segmentation and contrast delineation for resistivity analysis, integrating advanced image processing and machine learning techniques. During initial analyses using only k-means, the resulting optimal value of the silhouette coefficient K was 2. After using bilateral filtering together with contrast-limited adaptive histogram equalization (CLAHE) and validation by an expert, the results were more representative, and six clusters were identified. Ultimately, this study seeks to improve the efficiency of petroleum exploration by providing a high-precision automated framework with segmentation and contrast delineation for resistivity analysis, integrating advanced image processing and machine learning techniques.