Image Enhancement Operations Research Articles

Detection the internal quality of watermelon seeds based on terahertz imaging technology combined with image smoothing and enhancement algorithm

AbstractThe cultivation processes of watermelon seed are often affected by issues such as empty shells and defects, resulting in significant losses. To obtain high‐quality seeds, the terahertz imaging technology combined with image smoothing and enhancement algorithm was proposed to reduce the noise and non‐obvious features caused by the influence in the imaging process and realize the non‐destructive, efficient, and accurate detection of the internal quality of watermelon seeds. Initially, a terahertz imaging system with a spatial resolution of 0.4 mm was used to acquire images of watermelon seeds with varying levels of fullness. Subsequently, denoising techniques, including Gaussian filtering, median filtering, bilateral filtering, discrete wavelet transformation denoising, wavelet denoising, and principal component analysis denoising, were used to handle the terahertz spectral images of watermelon seeds in the frequency range of 1–1.5 THz, respectively. Image enhancement operations, involving segmented linear gray‐level transformation and fractional‐order differentiation, were performed on the terahertz images of watermelon seeds after denoising. The optimal image processing approach was determined based on defect assessment through threshold segmentation. Finally, the validation was conducted at a spatial resolution of 0.2 mm. The images at a spatial resolution of 0.4 mm were subjected to wavelet denoising and window slicing in segmented linear gray‐level transformation (WS‐SLT) enhancement; the results exhibited the following improvements in defect accuracy compared with untreated THz images. A 7.74% increase in accuracy was observed for empty seeds, along with a 6.29% increase in the defect ratio for defective seeds 1. The defect ratio for intact seeds was 0, and there was no significant difference in defect ratio accuracy for defective seeds 2. At a spatial resolution of 0.2 mm, the average defect ratio error of THz imaging handled by wavelet denoising and WS‐SLT was approximately 5.04%. In conclusion, the terahertz imaging technology coupled with wavelet denoising and WS‐SLT methods can be used to enhance the accuracy of internal defect detection in watermelon seeds, and it provides a technical foundation and reference for assessing watermelon seed fullness.

Implementation of Image Enhancement and Edge Detection Algorithm on Diabetic Retinopathy (DR) Image Using FPGA

Diabetic retinopathy (DR) is an ocular ailment that may lead to loss of vision and eventual blindness among individuals diagnosed with diabetes. The blood vessels of the retina, a layer of light-sensitive tissue located at the posterior aspect of the ocular globe, are adversely impacted. The identification of DR entails the utilization of retinal fundus images. The detection of any form of abnormality in the eye through raw fundus images poses a significant challenge for medical practitioners. Hence, it is imperative to engage in the processing of fundus images. This paper delineates several image processing techniques for DR images, including but not limited to, manipulation of brightness levels, application of negative transformation, and utilization of threshold operations. It focuses on elucidating the enhancement techniques that pertain to DR images, which aim to optimize the visual quality of said images in order to facilitate more facile disease detection. The process of detecting edges within DR images is also executed by Sobel edge detection algorithm. In order to successfully execute the aforementioned algorithms, expedient and contemporaneous systems are favored to account for the intricacies of the image processing calculations. The exclusive utilization of software techniques in order to fulfill the prerequisites of advanced algorithms presents a significant challenge, owing to the multifarious processes that are involved in their computation, coupled with an exigent requirement for high processing speeds. The proposed model is utilized to articulate a proficient model for the design and execution of field programable gate array (FPGA)-based image enhancement processes along with the Sobel edge detection algorithm upon DR images. Finally, a Internet Protocol chip is developed that can combine multiple image enhancement operations into a single framework with less complexity.

Not just select samples, but exploration[formula omitted] Genetic programming aided remote sensing target detection under deep learning

The data of target detection in remote sensing images are diverse, and the detection results of some categories with a small number of samples are poor. In order to solve this problem, most of the existing methods focus on the category with a small number of samples through data augmentation, but this will bring huge loss of original information, resulting in the decline of the effectiveness of some categories when improving the effectiveness. Additionally, since remote sensing image targets are small, numerous and densely distributed, the mixing degree of target and background is high, making them hardly distinguished. Therefore, a loss-based sample selection mechanism is proposed to enhance the category samples with low proportion. In the training process, we select between the original samples and enhanced samples through loss feedback, so as to retain the original sample information as much as possible and improve the detection performance. On this basis, an auxiliary feature detection module is proposed. First, the module detects the highly mixed area between the object to be detected and the background, and uses a series of image enhancement operations to build a genetic programming (GP) tree to separate the object from the background as much as possible, so that the detector can better extract and detect target features. Compared with other latest related algorithms, the loss-based sample selection mechanism and evolutionary auxiliary feature detection method proposed in this paper can improve the detection performance of low proportion categories through the sample selection mechanism, and improve the robustness to background clutter interference through evolutionary auxiliary feature detection. The proposed approach effectively improves the detection performance and performs well in remote sensing target detection.

Continuous word level sign language recognition using an expert system based on machine learning

The study of sign language recognition systems has been extensively explored using many image processing and artificial intelligence techniques for many years, but the main challenge is to bridge the communication gap between specially-abled people and the general public. This paper proposes a python-based system that classifies 80 words from sign language. Two different models have been proposed in this work: You Only Look Once version 4 (YOLOv4) and Support Vector Machine (SVM) with media-pipe. SVM utilizes the linear, polynomial and Radial Basis Function (RBF) kernels. The system does not need any additional pre-processing and image enhancement operations. The image dataset used in this work is self-created and consists of 80 static signs with a total of 676 images. The accuracy of SVM with media-pipe is 98.62% and the accuracy of YOLOv4 obtained is 98.8% which is higher than the existing state-of-the-art methods. An expert system is also proposed which utilizes both the above models to predict the hand gesture more accurately in real-time.

A Novel Multi-Task Learning Network Based on Melanoma Segmentation and Classification with Skin Lesion Images.

Melanoma is known worldwide as a malignant tumor and the fastest-growing skin cancer type. It is a very life-threatening disease with a high mortality rate. Automatic melanoma detection improves the early detection of the disease and the survival rate. In accordance with this purpose, we presented a multi-task learning approach based on melanoma recognition with dermoscopy images. Firstly, an effective pre-processing approach based on max pooling, contrast, and shape filters is used to eliminate hair details and to perform image enhancement operations. Next, the lesion region was segmented with a VGGNet model-based FCN Layer architecture using enhanced images. Later, a cropping process was performed for the detected lesions. Then, the cropped images were converted to the input size of the classifier model using the very deep super-resolution neural network approach, and the decrease in image resolution was minimized. Finally, a deep learning network approach based on pre-trained convolutional neural networks was developed for melanoma classification. We used the International Skin Imaging Collaboration, a publicly available dermoscopic skin lesion dataset in experimental studies. While the performance measures of accuracy, specificity, precision, and sensitivity, obtained for segmentation of the lesion region, were produced at rates of 96.99%, 92.53%, 97.65%, and 98.41%, respectively, the performance measures achieved rates for classification of 97.73%, 99.83%, 99.83%, and 95.67%, respectively.

MSLF-Net: A Multi-Scale and Multi-Level Feature Fusion Net for Diabetic Retinopathy Segmentation.

Diabetic Retinopathy (DR) is a diabetic complication that predisposes patients to visual impairments that could lead to blindness. Lesion segmentation using deep learning algorithms is an effective measure to screen and prevent early DR. However, there are several types of DR with varying sizes and high inter-class similarity, making segmentation difficult. In this paper, we propose a supervised segmentation method (MSLF-Net) based on multi-scale-multi-level feature fusion to achieve accurate end-to-end DR lesion segmentation. MSLF-Net builds a Multi-Scale Feature Extraction (MSFE) module to extract multi-scale information and provide more comprehensive features for segmentation. This paper further introduces the Multi-Level Feature Fusion (MLFF) module to improve feature fusion using a cross-layer structure. This structure only fuses low- and high-level features of the same class based on category supervision, avoiding feature contamination. Moreover, this paper produces additional masked images for the dataset and performs image enhancement operations to ensure that the proposed method is trainable and functional on small datasets. The extensive experiments are conducted on public datasets IDRID and e_ophtha. The results showed that our proposed feature enhancement method can perform feature fusion more effectively. Therefore, In the end-to-end DR segmentation neural network model, MSLF Net is superior to other similar models in segmentation, and can effectively improve the DR lesion segmentation performance.

A three-way clustering approach using image enhancement operations

Three-way clustering receives its motivation from three-way decisions. It uses the core set and support set to describe a cluster. The two sets divide clustering results into three parts or regions called inside, outside, and partial. The division helps identify the central core and outer sparse regions of a cluster, which is useful when the clusters have dense regions but also have vague boundaries. One of the main challenges in three-way clustering is the meaningful construction of the two sets and three regions. In this article, we introduce a blurring and sharpening inspired three-way clustering algorithm or BS3 for short. We first explain the use of blurring and sharpening operations to create a three-way representation for a typical object in an image in the form of central primary (the clear part of the object), blurry (the unclear part of the object), and the non-object part. Next, by realizing similarities between the object and a cluster, we define cluster blur and cluster sharp operations to create a three-way representation for clusters. Experimental results on real-world and synthetic datasets suggest that BS3 is comparable to best performing approaches and in many cases has superior results.

Pipeline 3D Modeling Based on High-Definition Rendering Intelligent Calculation

In the processing of panoramic video, projection mapping is a very critical step. The selection of the projection mapping format will affect the performance, transmission mode, and rendering mode of the panoramic video codec. Therefore, this article starts from the projection mapping format, analyzes the mapping process of the standard mapping format, and then proposes a method of rendering panoramic video in the projection mapping format. By analyzing the parallel design schemes of swarm intelligence algorithms under different granularities, this paper proposes a parallel swarm intelligence optimization algorithm design method and then designs and implements a parallel artificial bee colony algorithm. With the help of the ArcGIS Engine development platform, this paper defines the interface for data exchange. With the support of Multipatch format data in ArcGIS software, through secondary development, the three-dimensional pipeline automatic modeling module is established, and the pipeline model is automatically generated. The digital construction and visualization of the company play a driving role. Based on the understanding of the characteristics of the pipeline image itself, combined with the analysis of the shortcomings of the existing methods, this paper proposes a new deep learning-based high-definition rendering solution for the pipeline image. In this paper, the pipeline image is preprocessed, and then the processed pipeline image is converted into a style pipeline image through the pipeline image style transfer technology, and the obtained style pipeline image is postprocessed to enhance the effect. The preprocessing of pipeline images mainly includes pipeline image enhancement and pipeline image filtering operations. Its purpose is to change the distribution of pipeline images to improve the quality of pipeline images and make them more suitable for subsequent style conversion. In the part of pipeline image style conversion, this paper proposes a new deep learning-based pipeline image high-definition rendering network, which consists of three subnetworks: pipeline image feature modeling module, feature model alignment module, and pipeline image re-rendering module. This article has conducted sufficient experiments to fully compare the processing results of the method proposed in this article and other existing methods and at the same time shows the high-quality high-definition rendering results. The experimental results verify the excellent performance of the method proposed in this paper.

A Review on Underwater Image Enhancement Using Deep Residual Framework

There are various factors such as absorption, refraction & the phenomenon of scattering of light by particles suspended in water that are responsible for distorted colors, low contrast & blurred details of original underwater images. The traditional approaches include pre-processing the image using a descattering algorithm. The super-resolution (SR) method is applied. But this method has limitation that major part of the high frequency information is lost during descattering. This paper comes up with a solution for underwater image enhancement using a deep residual framework. Firstly, the generation of synthetic underwater images takes place for which cycle-consistent adversarial networks (CycleGAN) is employed. Further, these synthetic underwater images are used as training data for convolution neural network models. Secondly, the introduction of very-deep super-resolution reconstruction model to underwater resolution applications is carried out. Using this, the underwater Resnet model is proposed. It acts as a residual learning model for underwater image enhancement operations. Furthermore, the training mode & loss function are improved. Then, a multi-term loss function is formed which comprises of proposed edge difference loss & mean squared error loss. An asynchronous training mode is also being proposed that improves the performance of the multi-term loss function. Lastly, the discussion of the impact of batch normalization takes place. After a comparative analysis & underwater image enhancements, we can say that detailed enhancement performance & color correction of these proposed methods are much efficient & superior to that of previous traditional methods & deep learning models.

Facial Expression Recognition Using Dynamic Local Ternary Patterns With Kernel Extreme Learning Machine Classifier

Rapid growth in advanced human-computer interaction (HCI) based applications has led to the immense popularity of facial expression recognition (FER) research among computer vision and pattern recognition researchers. Lately, a robust texture descriptor named Dynamic Local Ternary Pattern (DLTP) developed for face liveness detection has proved to be very useful in preserving facial texture information. The findings motivated us to investigate DLTP in more detail and examine its usefulness in the FER task. To this end, a FER pipeline is developed, which uses a sequence of steps to detect possible facial expressions in a given input image. Given an input image, the pipeline first locates and registers faces in it. In the next step, using an image enhancement operator, the FER pipeline enhances the facial images. Afterward, from the enhanced images, facial features are extracted using the DLTP descriptor. Subsequently, the pipeline reduces dimensions of the high-dimensional DLTP features via Principal Component Analysis (PCA). Finally, using the multi-class Kernel Extreme Learning Machine (K-ELM) classifier, the proposed FER scheme classifies the features into facial expressions. Extensive experiments performed on four in-the-lab and one in-the-wild FER datasets confirmed the superiority of the method. Besides, the cross-dataset experiments performed on different combinations of the FER datasets revealed its robustness. Comparison results with several state-of-the-art FER methods demonstrate the usefulness of the proposed FER scheme. The pipeline with a recognition accuracy of 99.76%, 99.72%, 93.98%, 96.71%, and 78.75%, respectively, on the CK+, RaF, KDEF, JAFFE, and RAF-DB datasets, outperformed the previous state-of-the-art.

Computerized classification of gastrointestinal polyps using stacking ensemble of convolutional neural network

In this paper, we have proposed a classification method of gastrointestinal polyps using the stacking ensemble technique. The ensemble method consisted of three fine-tuned deep convolutional neural network architectures (Xception, ResNet-101, and VGG-19), and the network weights were initialized from the ImageNet dataset. Besides, this paper presented a multi-attribute decision-making technique-based frame selection method utilizing several measures of a suitable frame. The frame selection procedure reduces the processing overhead of the system and attained better classification results. Moreover, this study applied a set of image enhancement operations to remove specular reflection, clipping unnecessary regions, contrast enhancement, and noise reductions. The specified classification method of polyps showed significant improvement in performance metrics on available public datasets. The five-fold cross-validated performance of the study has an accuracy of 98.53 ± 0.62%, recall score of 96.17 ± 0.87%, a precision value of 92.09 ± 4.62%, a specificity score of 98.97 ± 0.36%, and an AUC score of 0.9912. This method can be helpful for endoscopists to make rigid decisions.

Digital Image Processing based Surface Area Calculation

The objective of current work is nondestructive measurement of surface area of regular or irregular shape just from image. Surface area calculation is mathematical part which needs to remember number of formulas and all for regular shape. It becomes more tedious if the shape whose area is to be calculated is irregular. In some cases such as mountain or lake measurement of dimension is also cumbersome task. To find the solution for such cases in today’s world of automation, the proposed work describes reference object based area calculation system which is based on different techniques of digital image processing. In this we have to click an image of object (whose area is to be calculated) along with one reference object with known surface area. Then the proposed system will perform image enhancement and segmentation operation and finally calculate the surface area of any 2-D surface. It is observed that the values obtained are having with good correlation with actual surface area values.

Multistage approach for automatic spleen segmentation in MRI sequences

Most of the known methods of segmentation of the abdominal organs are not automated for the whole series of images or are semi-automatic and require additional intervention by the user. This is typical for cases where the difference in intensity of the grey level between the subject and the background is small. This paper presents a multistage approach for spleen segmentation from MRI-sequences. It is based on segmentation methods such as active contours without edges and k-mean clustering. The proposed approach consists of some basic stages. The first stage is pre-processing, based on image enhancement and morphological operation. Two atlas models are created, which are used in the initial image to define the initial contour at which the segmentation begins. The proposed approach allows extracting the spleen in the different depth images, which has a variable form and unstable position. The conducted experiments are showing the robustness of the proposed approach. The obtained results demonstrate the effectiveness of the approach for application in screening diagnostics.

Road Marking Segmentation Based on Siamese Attention Module and Maximum Stable External Region

Lane detection severs as one of the pivotal techniques to promote the development of local navigation and HD Map building of autonomous driving. However, lane detection remains an unresolved problem for the challenge of detection accuracy in diverse driving scenarios and computational limitation in on-board devices, let alone other road guidance markings. In this paper, we go beyond aforementioned limitations and propose a segmentation-by-detection method for road marking extraction. The architecture of this method consists of three modules: pre-processing, road marking detection and segmentation. In the pre-processing stage, image enhancement operation is used to highlight the contrast especially between road markings and road background. To reduce the computational complexity, the road region will be cropped by vanishing point detection algorithm in this module. Then, a lightweight network is dedicated designed for road marking detection. In order to enhance the network sensitivity to road markings and improve the detection accuracy, we further incorporate a Siamese attention module by integrating with the channel and spatial maps into the network. In the segmentation module, different from the method of semantic segmentation by neural network, our segmentation method is mainly based on conventional image morphological algorithms, which is less computational and also can achieve pixel-level accuracy. Additionally, the sliding search box and maximum stable external region (MSER) algorithms are utilized to compensate for missed detection and position error of bounding boxes. In the experiments, our proposed method delivers outstanding performances on cross datasets and achieves the real-time speed on the embedded devices.

Flexible and efficient mobile optical mark recognition

Image processing methods that take place in different areas of life from identification to traffic flow control are starting to be used on mobile devices. Hence, usage of optical mark recognition systems (OMRS) on mobile devices is also not widespread as it is for image processing applications. A mobile OMRS is developed using image processing methods. In this developed system, image enhancement, edge detection, and tilt-shifting operations were performed on the optical answer paper images that are obtained using mobile devices. Within the scope of this study, it is aimed to achieve the maximum success of image processing operations on a mobile device that has limited hardware resources. The evaluation of optical answer papers that are obtained using mobile devices is performed using the image processing methods and algorithms developed within the scope of this study. The developed system is tested using different type of optical answer papers. According to the test results, the developed system provides a flexible and efficient use and partly eliminates previous work constraints. The success rate of the developed system is determined to be 93.54% using the test results obtained.

Kaiser-Hamming pencere yapısı ve Huang dönüşümü kullanılarak iki boyutlu sayısal süzgeç tasarımı ve imge iyileştirme uygulaması

Two dimensional digital filters which perform spatial filtering or frequency domain filtering are used for image enhancement operations. For high resolution image enhancement operations, two-dimensional digital filters which perform filtering in the frequency domain are preferred due to the computational advantage. However, in frequency domain based two dimensional digital filter design methods, there are various issues such as excess of computational complexity, suboptimal bad filter performance, and lack of elasticity. Moreover, in many design methods proposed in the literature, it is seen that there is no application on the image. The proposed method in this study presents a solution with improved filter performance and elasticity in less computational complexity. The proposed method, in which the effect on image enhancement is also studied, is based on the derivation of a two-dimensional Kaiser-Hamming window family obtained using the Huang’s method. The proposed method provides the design of many two-dimensional digital filters with various filter characteristics at a fixed length. Simulation results obtained from various filtering examples for two images show that two dimensional digital filters designed by the proposed method can provide different smoothing and sharpening on a wide scale according to the desired level for image enhancement applications such as image smoothing and image sharpening, and also the length and independent shape parameters of the proposed window have opposite effects on the image.

Single Image Rain Removal via a Simplified Residual Dense Network

The single-image rain removal problem has attracted tremendous interests within the deep learning domains. Although deep learning based de-raining methods outperform many conventional methods, there are still unresolved issues in regards to improving the performance. In this paper, we propose a simplified residual dense network (SRDN) to improve the de-raining performance and cut down the computation time. Inspired by the image processing domain knowledge that a rainy image can be decomposed into a base (low-pass) layer and a detail (high-pass) layer, we train our network by directly learning the residual between the detail layer of rainy images and the detail layer of clean images. It can both significantly reduce the mapping range from input to output and easily employ the image enhancement operation to handle the heavy rain with hazy looks. Instead of designing a deeper network structure to increase the learning ability of network, we propose a simplified dense block to explore more effective information between layers and, hence, reduce the computation time of network. Experiments on both synthetic and real-world images demonstrate that our SRDN network can achieve competitive results in comparison with the benchmarked and conventional approaches for single-image rain removal.

High Speed Energy Efficient Static Segment Adder for Approximate Computing Applications

Real time high quantity digital data computing design needs to achieve high performance with required accuracy range. The constraints involved with high performance are low power consumption, area efficiency and high speed. This paper proposes a design of high speed energy efficient Static Segment Adder (SSA), which improves the overall performance based on static segmentation. Accuracy Adjustment Logic (AAL) is incorporated to improve the accuracy derived from negating lower order bytes of input operands. In this paper, an integration of static segment method and accuracy adjustment logic is used to achieve computational accuracy for error tolerant applications. The proposed adder design enables to provide high speed and energy efficiency through the static segmentation method. Image enhancement operation is carried out using proposed SSA design. In this method, 99.4% overall computational accuracy for 16-bit addition even with 8-bit adder can be achieved.

Low-level pixelated representations suffice for aesthetically pleasing contrast adjustment in photographs

Today?s web-based automatic image enhancement algorithms decide to apply an enhancement operation by searching for ?similar? images in an online database of images and then applying the same level of enhancement as the image in the database. Two key bottlenecks in these systems are the storage cost for images and the cost of the search. Based on the principles of computational aesthetics, we consider storing task-relevant aesthetic summaries, a set of features which are sufficient to predict the level at which an image enhancement operation should be performed, instead of the entire image. The empirical question, then, is to ensure that the reduced representation indeed maintains enough information so that the resulting operation is perceived to be aesthetically pleasing to humans. We focus on the contrast adjustment operation, an important image enhancement primitive. We empirically study the efficacy of storing a pixelated summary of the 16 most representative colors of an image and performing contrast adjustments on this representation. We tested two variants of the pixelated image: a ?mid-level pixelized version? that retained spatial relationships and allowed for region segmentation and grouping as in the original image and a ?low-level pixelized-random version? which only retained the colors by randomly shuffling the 50 x 50 pixels. In an empirical study on 25 human subjects, we demonstrate that the preferred contrast for the low-level pixelized-random image is comparable to the original image even though it retains very few bits and no semantic information, thereby making it ideal for image matching and retrieval for automated contrast editing. In addition, we use an eye tracking study to show that users focus only on a small central portion of the low-level image, thus improving the performance of image search over commonly used computer vision algorithms to determine interesting key points.

Identifying Alluvial fans features using multispectral image processing techniques in selected area, northern Iraq

Landsat 7 Enhanced Thematic Mapper Plus image data was used to identify and map alluvial fans features in Shaikh Ibrahim anticline northern Iraq. In the present study, an active flowchart was design to extract and determine alluvial fans using digital image processing operations and digital elevation analysis. Two image processing methods including (image transformation and image enhancement) were used. Image transformation including principal component analysis (PCA) and ratio images (RI) analysis, while image enhancement operation including false color composite images (FCC). In image transformation processing, Based on the detailed analysis of eigenvectors and eigenvalues derived from different combinations of (PCA), five (PC) images of bands (13457) have been extracted and –PC4 has relatively strong positive loading for band 5 (0.632166) and high negative loading for band 7 (-0.369417) consequently, PC4 was given effective delineation about the major alluvial fans at the southwestern footslope from Shaikh Ibrahim anticline. In ratio image analysis, A combination of (RI) based on the spectral characteristics of alluvial fans materials were selected. Image enhancement includes A false color image of the subscene was produced by assigning band (741). This color composite was found to be useful in emphasizing to the all materials of alluvial fans. Digital elevation models (DEM) were used for describing topographic features related to the alluvial fans.