Multidimensional Contrast Limited Adaptive Histogram Equalization

Contrast enhancement is an important preprocessing technique for improving the performance of downstream tasks in image processing and computer vision. Among the existing approaches based on nonlinear histogram transformations, contrast limited adaptive histogram equalization (CLAHE) is a popular choice when dealing with 2D images obtained in natural and scientific settings. The recent hardware upgrade in data acquisition systems results in significant increase in data complexity, including their sizes and dimensions. Measurements of densely sampled data higher than three dimensions, usually composed of 3D data as a function of external parameters, are becoming commonplace in various applications in the natural sciences and engineering. The initial understanding of these complex multidimensional datasets often requires human intervention through visual examination, which may be hampered by the varying levels of contrast permeating through the dimensions. We show both qualitatively and quantitatively that using our multidimensional extension of CLAHE (MCLAHE) acting simultaneously on all dimensions of the datasets allows better visualization and discernment of multidimensional image features, as are demonstrated using cases from 4D photoemission spectroscopy and fluorescence microscopy. Our implementation of multidimensional CLAHE in Tensorflow is publicly accessible and supports parallelization with multiple CPUs and various other hardware accelerators, including GPUs

Contrast enhancement by multi-scale adaptive histogram equalization

An approach for contrast enhancement utilizing multi-scale analysis is introduced. Sub-band coefficients were modified by the method of adaptive histogram equalization. To achieve optimal contrast enhancement, the sizes of sub-regions were chosen with consideration to the support of the analysis filters. The enhanced images provided subtle details of tissues that are only visible with tedious contrast/brightness windowing methods currently used in clinical reading. We present results on chest CT data, which shows significant improvement over existing state-of-the-art methods: unsharp masking, adaptive histogram equalization (AHE), and the contrast limited adaptive histogram equalization (CLAHE). A systematic study on 109 clinical chest CT images by three radiologists suggests the promise of this method in terms of both interpretation time and diagnostic performance on different pathological cases. In addition, radiologists observed no noticeable artifacts or amplification of noise that usually appears in traditional adaptive histogram equalization and its variations

Keselesaan ke tempat kerja mempengaruhi kualiti hidup masyarakat bandar di Mukim Kajang, Selangor

Keselesaan penduduk ke tempat bekerja boleh mempengaruhi kualiti hidup masyarakat. Kajian ini telah mengenalpasti bahawa keselesaan ke tempat bekerja merupakan aspek yang boleh menyumbang kepada kualiti hidup yang baik. Oleh itu, aspek keselesaan ke tempat kerja harus diambilkira dalam menilai kualiti hidup masyarakat di bandar. Permasalah kajian ini ialah isu ketidakselesaan penduduk ke tempat bekerja telah menyebabkan gangguan pada kualiti hidup. Objektif kajian ialah mengkaji persepsi penduduk di bandar terhadap keselesaan ke tempat kerja. Kajian dijalankan di Bandar Kajang dan Bandar Baru Bangi. Kaedah soal selidik telah digunakan di lapangan. Sejumlah 700 responden telah dipilih secara rawak bebas di kawasan kajian. Data-data yang dikumpul telah dianalisis menggunakan program SPSS. Hasil kajian mendapati paling ramai responden iaitu 135 orang bekerja di tempat lain-lain, 112 bekerja di Kajang,105 bekerja di Bandar Baru Bangi, 36 bekerja di Universiti Kebangsaan Malaysia dan 26 bekerja di Serdang. Analisis perkaitan antara jarak rumah ke tempat bekerja menunjukkan nilai khi kuasa dua sebanyak 89.329 dan signifikan pada aras 0.05 (p=0.000), perkaitan antara jarak rumah ke tempat kerja dengan tempoh terlibat kesesakan lalu lintas menunjukkan nilai khi kuasa dua sebanyak 227.568 dan signifikan pada aras 0.05 (p=0.000). Sejumlah 208 responden terganggu emosi semasa berhadapan dengan kesesakkan lalu lintas, manakala 150 menyatakan masih boleh bersabar. Seramai 359 responden menyatakan tidak selesa ke tempat kerja sekiranya berlaku kesesakan lalu lintas.Kajian merumuskan bahawa kajian keselesaan ke tempat kerja wajar digunakan sebagai penunjuk kualiti hidup masyarakat di bandar, berdasarkan konflik-konflik yang dialami penduduk di bandar semasa berinteraksi ke tempat bekerja

Optimum Illuminant Determination Based on Reduced and Optimized Multispectral Spectroscopy to Enhance Vein Detection

Venepuncture as a mode of gaining intravenous access has been a prime practice in surgical procedures and other conventional drug administering into a patient. Biomedical engineering has stressed relatively high scale of importance in the spectroscopic analysis of vein imaging as a sparky approach to promote a non-invasive catheterization. However, medical personnel are challenged by the physiological circumstances of skin tone, presence of scars and irregularity of the epidermal topology, when performing subcutaneous vein localization, which led them to increase number of insertion attempts. Hence, this paper proposes an optimized solution to provide enhanced visual aids for personnel to achieve successful vein catheterization at first attempt

Monitoring climate change effects on coral reefs using edge-based image segmentation

Coral reefs are valuable ecosystems that face vulnerability to climate change impacts. Underwater images often encounter noise from various factors, such as water turbidity, lighting conditions, attenuation, and scattering, which can complicate edge detection and segmentation processes, leading to inaccuracies. However, image processing techniques offer a viable solution to this issue. In this study, an edge-based segmentation approach is proposed that uses multiple contrast techniques to detect and quantify changes in coral reef imagery. The proposed approach effectively identifies changes in coral reef imagery, making it a valuable tool for monitoring climate change's effects on these ecosystems. Furthermore, high-resolution images at different time points and locations were collected, and then an edge-based segmentation approach was utilized to enhance the accuracy of edge detection and segmentation. Comparing the proposed method with traditional segmentation techniques showed a significant improvement in terms of segmentation precision. Subsequently, alterations in the structure and composition of coral reefs are observed, indicating the influence of climate change on these ecosystems. This research highlights the capabilities of image processing techniques using edge-based segmentation in monitoring coral reefs. It offers an effective and precise approach to detecting changes in coral reef images, thereby contributing to conservation endeavors

Local Contrast Enhancement Utilizing Bidirectional Switching Equalization Of Separated And Clipped Sub-Histograms

Digital image contrast enhancement methods that are based on histogram equalization (HE) technique are useful for the use in consumer electronic products due to their simple implementation. However, almost all the suggested enhancement methods are using global processing technique, which does not emphasize local contents. Kaedah penyerlahan beza jelas imej digit berdasarkan teknik penyeragaman histogram adalah berguna dalam penggunaan produk elektronik pengguna disebabkan pelaksanaan yang mudah. Walau bagaimanapun, kebanyakan kaedah penyerlahan yang dicadangkan adalah menggunakan teknik proses sejagat dan tidak menekan kepada kandungan setempat

Performance Evaluation of RBF, Cascade, Elman, Feed Forward and Pattern Recognition Network for Marathi Character Recognition with CLAHE Feature Extraction Method

The purpose of this paper is to study, analyze and improve the performance of RBF, Cascade, Elman, Feed Forward and Pattern Recognition Networks using �Contrast-limited Adaptive Histogram Equalization method� of featureextraction. This work is divided in to two sections. In the earlier work, we have performed the performance analysis of RBF neural network, Cascade Neural network, Elman Neural network and Feed forward neural network for the character recognition of handwritten Marathi curve scripts using �Edge detection and Dilation method� of feature extraction. In this paper, we have applied the feature extraction methodknown as Contrast-limited Adaptive Histogram Equalization (CLAHE). This feature extraction method enhances the contrast of images by transforming the values in the intensity image. For this experiment, we have considered the six samples each of 48 Marathi characters. For every sampled character, the CLAHE feature extraction method is applied. Then we have studied and analyzed the performance of these five Neural Networks for character recognition. It is found that except Elman Network, the performance of rest of all the networks is increased

Importance of Image Enhancement Techniques in Color Image Segmentation: A Comprehensive and Comparative Study

Color image segmentation is a very emerging research topic in the area of color image analysis and pattern recognition. Many state-of-the-art algorithms have been developed for this purpose. But, often the segmentation results of these algorithms seem to be suffering from miss-classifications and over-segmentation. The reasons behind these are the degradation of image quality during the acquisition, transmission and color space conversion. So, here arises the need of an efficient image enhancement technique which can remove the redundant pixels or noises from the color image before proceeding for final segmentation. In this paper, an effort has been made to study and analyze different image enhancement techniques and thereby finding out the better one for color image segmentation. Also, this comparative study is done on two well-known color spaces HSV and LAB separately to find out which color space supports segmentation task more efficiently with respect to those enhancement techniques.Comment: 27 pages, 17 figures, 2 Tables, 1 flowchar

Color Image Enhancement via Combine Homomorphic Ratio and Histogram Equalization Approaches: Using Underwater Images as Illustrative Examples

The histogram is one of the important characteristics of grayscale images, and the histogram equalization is effective method of image enhancement. When processing color images in models, such as the RGB model, the histogram equalization can be applied for each color component and, then, a new color image is composed from processed components. This is a traditional way of processing color images, which does not preserve the existent relation or correlation between colors at each pixel. In this work, a new model of color image enhancement is proposed, by preserving the ratios of colors at all pixels after processing the image. This model is described for the color histogram equalization (HE) and examples of application on color images are given. Our preliminary results show that the application of the model with the HE can be effectively used for enhancing color images, including underwater images. Intensive computer simulations show that for single underwater image enhancement, the presented method increases the image contrast and brightness and indicates a good natural appearance and relatively genuine color