Variation Of Pixel Values Research Articles

Role Of Digital Image Processing In Education And Medical Field

Digital image processing is a technique that uses computer algorithms to analyze images and transform images into better ones.It is an important branch of Telecommunication engineering that deals with the improvisation of reliability and accuracy of the digital communication by employing multiple techniques. Image processing involves many processes such as taking images, preprocessing, image enhancement, transformation and analysis. Variations in pixel values are one of the major challenges faced by image processing. Digital Signal Processing is at the core of virtually all of today's information technology, and its impact is felt everywhere in telecommunications, medical technology, radar and sonar, and in seismic data analysis.Digital Image Processing plays an important role in medical field and education. Various researches has been done and currently popularly used. In education, pictures can be used to enhance student learning and to develop students’ self-esteem, or their utilization can be of little or no value and, if used carelessly, can, in fact, undermine students' self-confidence. Digital health technologies include both hardware and software solutions and services, including telemedicine, wearable devices, augmented reality, and virtual reality. Generally, digital health interconnects health systems to improve the use of computational technologies, smart devices, computational analysis techniques, and communication media to aid healthcare professionals and their patients manage illnesses and health risks, as well as promote health and wellbeing. Digital imaging gives healthcare providers better and more accurate images, which leads to establishing diagnoses sooner, making treatment of the patient more successful.

Use of different vegetation indices for the evaluation of the kinetics of the cherry tomato (Solanum lycopersicum var. cerasiforme) growth based on multispectral images by UAV

Abstract This study evaluated seven vegetation indices for the monitoring of a cherry tomato crop using an unmanned aerial vehicle with a multispectral camera that measures in the green, red, and near-infrared spectral bands. A photogrammetric flight plan was designed to capture the spectral images every 2 weeks in two agricultural parcels identified as Treatment 1 ( T 1 {T}_{1} ) and Treatment 2 ( T 2 {T}_{2} ). The corresponding orthophotographs were obtained using digital photogrammetry techniques. Subsequently, vegetation indices were calculated for these orthophotographs. The mean and standard deviation of these indices were extracted, and a statistical analysis was performed to compare the vegetation indices and to analyze their behavior over time. Analysis of variance showed that the ratio vegetation index (RVI), green vegetation index (GVI), normalized difference vegetation index (NDVI), infrared percentage vegetation index (IPVI), green normalized difference vegetation index (GNDVI), and optimized soil-adjusted vegetation index (OSAVI) indices showed significant variation (P-value <0.05) over time. No statistically significant differences between the two treatments were found. IPVI, NDVI, and OSAVI showed less variation in pixel values. RVI, GVI, NDVI, IPVI, GNDVI, and OSAVI proved to be valuable tools for monitoring field crops since these indices responded to the crop growth kinetics.

Celestial Object Detection in Astronomical Images Using MSE and Jacobi Moments

Astronomical images share a common characteristic, which is low variability. This term refers to images with minimal changes or variations in pixel values or features. In this article, we leverage the low variability characteristic of astronomical images to detect celestial objects. We compute the mean squared error for the reconstructed astronomical image, derived from its Jacobi moments. These moments are calculated multiple times, focusing on different regions within the image. The mean squared error metric serves as the score function for the simulated annealing algorithm, aiding in the identification of regions with the highest information loss. The parameters used to compute Jacobi moments to focus on that region are then interpreted as the coordinates of celestial objects. This method proves effective for preprocessing images since it provides optimal parameters for Jacobi polynomials, which will enhance their feature extraction capability. Additionally, it serves as an object detection method, as we can interpret the Jacobi moments' parameters as coordinates for the objects within the images.

Multi visual images fusion approach for metro tunnel defects based on saliency optimization of pixel level defect image features

The multi-vision defect sensing system, lining composed primarily of IRT and RGB cameras, allows for automatic identification and extraction of small surface ailments, greatly enhancing detection efficiency. However, the presence of various issues like train vibration, inconsistent lighting, fluctuations in temperature and humidity leads to the images showing inadequate uniformity in illumination, blurriness, and a decrease in the level of detail. The above issues have led to unsatisfactory fusion processing results for multiple visual images and increased missed detection rates. To address the above-mentioned issue, multi visual images fusion approach for subway tunnel defects based on saliency optimization of pixel level defect image features is proposed. The approach initially analyses the train’s motion status and image blurring conditions. It then eliminates the dynamic blurring in the image. Secondly, Image weights are allocated based on the uniformity of visible light image illumination in the tunnel, as well as real-time temperature and humidity. Finally, image feature extraction and fusion are performed by a U-Net network that integrates channel attention mechanisms. The entire experiment was carried out on a dataset consisting of leakage data from the tunnel lining of Shanghai Metro and tunnel defect data from Beijing Metro. The experimental results demonstrate that this approach improves the image pixel value variation rate by 39.7%, enhances the edge quality by 23%, and outperforms similar approach in terms of average gradient, gradient quality, and sum of difference correlation with improvements of 15.9%, 7.3%, and 26.6% respectively.

Optimum Spatial Resolution of Satellite-based Optical Sensors for Maximizing Classification Performance

Optimum spatial resolution of satellite based optical sensors for maximizing classification performance is investigated. Also, classification performance assessment method considering spatial resolution of satellite based optical imagers is proposed. Optimum spatial resolution which makes the highest classification accuracy is determined from spatial frequency components, spectral features of objects and classification method. First, in this paper, based on the relationship between variance of pixels and classification accuracy, classification accuracy for Landsat Multiple Spectral Scanner: MSS images with various Instantaneous Field of View (IFOV) will be shown. In their connection, variance of pixel values for images with various IFOV will be clarified. Second, assuming the shape of boundary line between adjacent categories is circle, relationship among IFOV, ratio of Mixels and classification accuracy will be cleared under the supposition that the number of Mixels equals to that of misclassified pixels. Finally, it will be also shown that aforementioned relationships and optimum spatial resolution have been confirmed by using airborne based MSS data of Sayama district in Japan.

Simple Shading Correction Method for Brightfield Whole Slide Imaging.

Whole slide imaging (WSI) refers to the process of creating a high-resolution digital image of a whole slide. Since digital images are typically produced by stitching image sequences acquired from different fields of view, the visual quality of the images can be degraded owing to shading distortion, which produces black plaid patterns on the images. A shading correction method for brightfield WSI is presented, which is simple but robust not only against typical image artifacts caused by specks of dust and bubbles, but also against fixed-pattern noise, or spatial variations in pixel values under uniform illumination. The proposed method comprises primarily of two steps. The first step constructs candidates of a shading distortion model from a stack of input image sequences. The second step selects the optimal model from the candidates. The proposed method was compared experimentally with two previous state-of-the-art methods, regularized energy minimization (CIDRE) and background and shading correction (BaSiC) and showed better correction scores, as smooth operations and constraints were not imposed when estimating the shading distortion. The correction scores, averaged over 40 image collections, were as follows: proposed method, 0.39 ± 0.099; CIDRE method, 0.67 ± 0.047; BaSiC method, 0.55 ± 0.038. Based on the quantitative evaluations, we can confirm that the proposed method can correct not only shading distortion, but also fixed-pattern noise, compared with the two previous state-of-the-art methods.

A novel scheme for image sharpness using inflection points

AbstractIt is useful to increase the sharpness in medical images. This improvement can help medical diagnoses and treatment outcomes for patients. Noise overshoot and oversharpening effects are common artifacts of conventional sharpening algorithms. In this work, we propose an adaptive sharpness algorithm that achieves better sharpening effects than standard methods. As the pixel value changes abruptly at an edge, a method that adequately emphasized sudden variations in pixel values is effective for use in the sharpening process. Measurement of the gradient norm value of each pixel is calculated and compared to a threshold value to produce a curve. The curve quickly drops at the initial stage and then decreased more slowly for higher norm values. To distinguish the edge, an inflection point is determined by taking the second derivative of the curve and identifying the turnover point (where the curvature changes sign). Norm values higher than the inflection point are identified as belonging to the edge, and a simple sharpness filter is adaptively applied to these points. The proposed approach yielded better results than global filtering and a conventional unsharp masking approach when evaluated using Pratt's figure of merit.

Performance analysis of transformation and Bogdonov chaotic substitution based image cryptosystem

<span lang="EN-US">In this article, a combined Pseudo Hadamard transformation and modified Bogdonav chaotic generator based image encryption technique is proposed. Pixel position transformation is performed using Pseudo Hadamard transformation and pixel value variation is made using Bogdonav chaotic substitution. Bogdonav chaotic generator produces random sequences and it is observed that very less correlation between the adjacent elements in the sequence. The cipher image obtained from the transformation stage is subjected for substitution using Bogdonav chaotic sequence to break correlation between adjacent pixels. The cipher image is subjected for various security tests under noisy conditions and very high degree of similarity is observed after deciphering process between original and decrypted images.</span>

Design and analysis of pseudo hadamard transformation and non-chaotic substitution based image encryption scheme

In this paper, Pseudo Hadamard Transformation (PHT) and non-chaotic substitution based image encryption scheme has been proposed. Images are characterized by intrinsic properties such as, strong redundancy and correlation between the adjacent pixels, hence more vulnerable to cyber-attacks. In the proposed technique, the redundancy and correlation have been effectively reduced by pixel position transformation using PHT and pixel value variation using non chaotic substitution, providing two stage security in encryption for images. Fifteen standard test images are considered for experimental analysis. Better average Number of Pixel Changing Rate (NPCR) and Unified Average pixel Changing Intensity (UACI) are obtained for a set of standard test images compared to more popular existing algorithms.

Design and Implementation of Transformation and Non-Chaotic Substitution Based Image Cryptosystem

In this article, a Modified Pseudo Hadamard Transformation (MPHT) and non-Chaotic substitution based image encryption scheme has been proposed. Due to intrinsic properties such as, strong redundancy and correlation between the adjacent pixels, images are more vulnerable to cyber-attacks. In the proposed technique, the redundancy and correlation have been effectively reduced by pixel position transformation using MPHT and pixel value variation using non chaotic substitution, providing two stage security in encryption for secrete images. An average 99.6089% of Number of Pixel Changing Rate (NPCR) and 33.4328% of Unified Average pixel Changing Intensity (UACI) are obtained for a set of standard test images compared to more popular existing techniques

Convolutional Neural Network Based Bi-prediction Utilizing Spatial and Temporal Information in Video Coding

With the growing popularity of high-resolution videos, the demand for higher coding efficiency is increasing to cope with multimedia transmission challenges on the communication network. Since conventional linearly weighted bi-prediction does not handle inhomogeneous motion activities inside one block well, in recent works, Convolutional Neural Network (CNN) is explored to tackle inhomogeneous motion by utilizing patch-level information to predict each individual pixel. However, as only two reference blocks are used as input information, those works ignore the variation of pixel values between reference blocks and current block, and ignore the differences between extrapolation and interpolation. This work utilizes both spatial neighboring pixels and temporal display orders as extra inputs for CNN models to further improve the prediction accuracy of a bi-predictor. The extra input information has the following advantages. First, variations among spatial neighboring pixels of both reference blocks and the current block reflect variations between current block and reference blocks. Together with temporal distance, spatial neighboring pixels are able to address extrapolation and interpolation uniformly. Second, spatial neighboring pixels of the current block have a high correlation with current predicted signals, which helps to reduce prediction residuals around the block boundary and alleviate block artifacts. Last, temporal distances help to improve the accuracy of prediction signals based on its ability of reflecting the correlation of video frames. Experimental results show that our proposed network achieves 2.92% and 5.09% bit-rate savings on average compared with HEVC, under Low-Delay B (LDB) and Random-Access (RA) configurations, respectively. As temporal information is used in our network, the LDB and RA configurations share the same networks in this work.

ADAPTIVE TRIMMED MEAN AUTOREGRESSIVE MODEL FOR REDUCTION OF POISSON NOISE IN SCINTIGRAPHIC IMAGES

A 2-D Adaptive Trimmed Mean Autoregressive (ATMAR) model has been proposed for denoising of medical images corrupted with poisson noise. Unfiltered images are divided into smaller chunks and ATMAR model is applied on each chunk separately. In this paper, two 5x5 windows with 40% overlapping are used to predict the center pixel value of the central row. The AR coefficients are updated by sliding both windows forward with 60% shift. The same process is repeated to scan the entire image for prediction of a new denoised image. The Adaptive Trimmed Mean Filter (ATMF) eradicates the lowest and highest variations in pixel values of the ATMAR model denoised image and also average out the remaining neighborhood pixel values. Finally, power-law transformation is applied on the resultant image of the ATMAR model for contrast stretching. Image quality is judged in terms of correlation, Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM) and Peak Signal to Noise Ratio (PSNR) of the image with latest denoising techniques. The proposed technique showed an efficient way to scale down poisson noise in scintigraphic images on a pixel-by-pixel basis.

Co‐occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition

It is challenging to recognize smoke from visual scenes due to large variations of smoke colors, textures and shapes. To improve robustness, we propose a novel feature extraction method based on similarity and dissimilarity matching measures of Local Binary Patterns (LBP). Given two bit‐sequences of an LBP code pair, the similarity and dissimilarity matching measures are defined as the ratios of the 1–1 bitwise matching number to the 0–0 bitwise matching number and the 1–0 number to the 0–1 number, respectively. To capture local code variations, we calculate the measures between LBP codes of a center pixel and its neighbors. Then we compare each measure with its global mean to propose Similarity Matching based Local Binary Patterns (SMLBP) and Dissimilarity Matching based Local Binary Patterns (DMLBP). Since SMLBP and DMLBP extract spatial variations of the 1st order LBP codes, they actually represent the 2nd order variations of pixel values. Furthermore, we adopt different mapping modes and multi‐scale neighborhoods to obtain rotation and scale invariances. Finally, we concatenate the histograms of LBP, SMLBP and DMLBP to generate a feature vector containing 1st and 2nd order information. Experiments show that our method obviously outperforms existing methods.

PENGARUH VARIASI ROTATION TIME TERHADAP NILAI DLP (DOSE LENGTH PRODUCT) DAN IMAGE NOISE PADA PESAWAT MSCT 128 SLICE (STUDI KASUS)

Background: The patient's dose and image noise illustrate the quality of the CT scan. DLP (Dose Lenght Product) is a dose description on a CT-Scan tool by providing a measure of the total amount of radiation exposure for a series of scans. While image noise is the difference / variation in pixel values contained in an image matrix. The use of rotation time in CT scan is the most likely to contribute to patient dose and image noise.
 Method: Toshiba Aquilion 128 MSCT aircraft and Acrilyc phantom were used as material in this study. The parameters on the CT-Scan tool used are the suitability test parameters, namely at kV 120, 400mA, slice thickness 2 mm, FOV 240. Rotation time variations on CT scan imaging that can be selected are in the range of values ​​of 0.35s, 0.375s, 0.4s , 0.45s, 0.5s, 0.6s, 0.75s, 1.0s while the other parameters are the same. From each rotation time value, the estimated dose value is the DLP value on the CT Scan monitor screen (Summary). Furthermore, the scanning results measure the noise value by measuring the ROI on the middle side of the area around a diameter of 2-3 cm2. The measurement results are recorded and entered into tables, graphs and tests with statistics, and test the effect (regression) as hypothesis testing with a significant value (α) <0.05. This research was conducted in the Radiology IRD unit of Dr. RSU. Soetomo.
 Results: Obtained DLP values ​​of 37.3 mGy.cm, 28.4 mGy.cm, 23.1 mGy.cm, 19.5 mGy.cm, 17.8 mGy.cm, 16 mGy.cm, 15.1 mGy.cm and 14.2 mGy.cm at rotation times of 1.0s, 0.75s, 0.6s, 0.5s, 0.45s, 0.4s, 0.375s, 0.35s. For the assessment of image noise the results are 5.6 HU, 5.5 HU, 5.3 HU, 5.1 HU, 4.8 HU, 4.5 HU, 4.1 HU and 3.4 HU at rotation time 0 , 35s, 0.375s, 0.4s, 0.45s, 0.5s, 0.6s, 0.75s, 0.5s, and 1s. From the above results obtained the value of DLP at the lowest radiation dose on the use of rotation time of 0.35 sec which is 14.2 mGy.cm and the largest at 1.0 sec is 37.3 mGy.cm, with a regression coefficient of 1,785 + 35,507 with a value of 0,000 so that the effect is very significant . In this case it shows that the contribution of rotation time effect on optimization of the estimated DLP value is 100%. Noise value at the variation of rotation time values ​​found that 0.35 s produces an optimum noise of 5.6 HU and 1.0 sec produces the smallest noise of 3.4 HU. This can be seen from the regression coefficient of 6,637 - 3,344 with a value (α) <0.05. In this case it shows that the contribution of rotation time to the image noise value is 97.8% while the remaining 2.2% is influenced by other variables contained in the model.
 Conclusion: The use of slower rotation time is still recommended to produce optimal image quality in certain cases, because the noise value is close to zero and is still within the limits of the normal noise value. Each increase in rotation time of one unit will increase the optimization value of the estimated DLP by 35.507, so that it can be said there is no significant difference even though the measurement looks very varied in value.

Hyperspectral Imaging Using Flexible Endoscopy for Laryngeal Cancer Detection.

Hyperspectral imaging (HSI) is increasingly gaining acceptance in the medical field. Up until now, HSI has been used in conjunction with rigid endoscopy to detect cancer in vivo. The logical next step is to pair HSI with flexible endoscopy, since it improves access to hard-to-reach areas. While the flexible endoscope’s fiber optic cables provide the advantage of flexibility, they also introduce an interfering honeycomb-like pattern onto images. Due to the substantial impact this pattern has on locating cancerous tissue, it must be removed before the HS data can be further processed. Thereby, the loss of information is to minimize avoiding the suppression of small-area variations of pixel values. We have developed a system that uses flexible endoscopy to record HS cubes of the larynx and designed a special filtering technique to remove the honeycomb-like pattern with minimal loss of information. We have confirmed its feasibility by comparing it to conventional filtering techniques using an objective metric and by applying unsupervised and supervised classifications to raw and pre-processed HS cubes. Compared to conventional techniques, our method successfully removes the honeycomb-like pattern and considerably improves classification performance, while preserving image details.

Temporal and Spatial Denoising of Depth Maps.

This work presents a procedure for refining depth maps acquired using RGB-D (depth) cameras. With numerous new structured-light RGB-D cameras, acquiring high-resolution depth maps has become easy. However, there are problems such as undesired occlusion, inaccurate depth values, and temporal variation of pixel values when using these cameras. In this paper, a proposed method based on an exemplar-based inpainting method is proposed to remove artefacts in depth maps obtained using RGB-D cameras. Exemplar-based inpainting has been used to repair an object-removed image. The concept underlying this inpainting method is similar to that underlying the procedure for padding the occlusions in the depth data obtained using RGB-D cameras. Therefore, our proposed method enhances and modifies the inpainting method for application in and the refinement of RGB-D depth data image quality. For evaluating the experimental results of the proposed method, our proposed method was tested on the Tsukuba Stereo Dataset, which contains a 3D video with the ground truths of depth maps, occlusion maps, RGB images, the peak signal-to-noise ratio, and the computational time as the evaluation metrics. Moreover, a set of self-recorded RGB-D depth maps and their refined versions are presented to show the effectiveness of the proposed method.

Characterizing bi-temporal patterns of land surface temperature using landscape metrics based on sub-pixel classifications from Landsat TM/ETM+

Landscape patterns in a region have different sizes, shapes and spatial arrangements, which contribute to the spatial heterogeneity of the landscape and are linked to the distinct behavior of thermal environments. There is a lack of research generating landscape metrics from discretized percent impervious surface area data (ISA), which can be used as an indicator of urban spatial structure and level of development, and quantitatively characterizing the spatial patterns of landscapes and land surface temperatures (LST). In this study, linear spectral mixture analysis (LSMA) is used to derive sub-pixel ISA. Continuous fractional cover thresholds are used to discretize percent ISA into different categories related to urban land cover patterns. Landscape metrics are calculated based on different ISA categories and used to quantify urban landscape patterns and LST configurations. The characteristics of LST and percent ISA are quantified by landscape metrics such as indices of patch density, aggregation, connectedness, shape and shape complexity. The urban thermal intensity is also analyzed based on percent ISA. The results indicate that landscape metrics are sensitive to the variation of pixel values of fractional ISA, and the integration of LST, LSMA. Landscape metrics provide a quantitative method for describing the spatial distribution and seasonal variation in urban thermal patterns in response to associated urban land cover patterns.

A NOVEL APPLICATION OF DIGITAL IMAGE TREATMENT BY QUANTITATIVE PIXEL ANALYSIS TO TRACE FOSSIL RESEARCH IN MARINE CORES

ABSTRACT Ichnological analysis in cores has grown substantially in the past two decades because of its importance in fields such as paleoecology, biostratigraphy, or reservoir characterization. Yet core analysis entails some added difficulties in comparison to ichnological outcrop study, especially in modern marine sediments. Quantitative pixel analysis conducted on high-resolution images is used to facilitate the study of bioturbation in marine cores from site U1385 of IODP Expedition 339 in the Gulf of Cadiz. Lateral and vertical variation of pixel values obtained from the infilling trace fossils and from the host sediment are revealed to be highly useful to: (1) differentiate between trace fossils and biodeformational structures, including the host sediment; (2) discriminate between ichnotaxa (i.e., Palaeophycus vs. Planolites); (3) differentiate between passively and actively infilled structures (e.g., Thalassinoides vs. Planolites), and (4) characterize the horizon of bioturbation and thus evaluate t...

변형된 방향성 마스크를 이용한 에지검출 방법에 관한 연구

Edge Detection is a technique that obtains the particular information of the image using the brightness variation of pixel values and utilized for preprocessing in various image processing sectors. The conventional edge detection methods such as Sobel, Prewitt and Roberts are processed by applying the same weighted value to the entire pixels regardless of pixel distrbution and provides somewhat insufficient edge detection results. therefore, this paper has proposed an edge detection method considering the direction and magnitute of pixels by applying a modified directional mask.

A novel method to reduce time investment when processing videos from camera trap studies.

Camera traps have proven very useful in ecological, conservation and behavioral research. Camera traps non-invasively record presence and behavior of animals in their natural environment. Since the introduction of digital cameras, large amounts of data can be stored. Unfortunately, processing protocols did not evolve as fast as the technical capabilities of the cameras. We used camera traps to record videos of Eurasian beavers (Castor fiber). However, a large number of recordings did not contain the target species, but instead empty recordings or other species (together non-target recordings), making the removal of these recordings unacceptably time consuming. In this paper we propose a method to partially eliminate non-target recordings without having to watch the recordings, in order to reduce workload. Discrimination between recordings of target species and non-target recordings was based on detecting variation (changes in pixel values from frame to frame) in the recordings. Because of the size of the target species, we supposed that recordings with the target species contain on average much more movements than non-target recordings. Two different filter methods were tested and compared. We show that a partial discrimination can be made between target and non-target recordings based on variation in pixel values and that environmental conditions and filter methods influence the amount of non-target recordings that can be identified and discarded. By allowing a loss of 5% to 20% of recordings containing the target species, in ideal circumstances, 53% to 76% of non-target recordings can be identified and discarded. We conclude that adding an extra processing step in the camera trap protocol can result in large time savings. Since we are convinced that the use of camera traps will become increasingly important in the future, this filter method can benefit many researchers, using it in different contexts across the globe, on both videos and photographs.