Image Enhancement Technology Research Articles

Image Enhancement Based on Histogram Equalization

Image enhancement is an important technology in the process of image processing. It can improve the image effect, enhance the overall brightness and clarity of the image. Histogram equalization is a very effective image enhancement technology. On the basis of probability theory, it uses gray point calculation to transform histogram. This paper mainly studies the basic theory of histogram equalization for image processing. At the same time, the image is experimentally analyzed by matlab. The results show that histogram equalization can improve the image effect and enhance the contrast of the image.

Wide-field underwater EMR followed by line-assisted complete closure for a large duodenal adenoma.

Wide-field underwater EMR followed by line-assisted complete closure for a large duodenal adenoma.

A Review of New and Emerging Techniques For Optical Diagnosis of Colonic Polyps.

Endoscopic imaging is a rapidly progressing field and benefits from miniaturization of advanced imaging technologies, which may allow accurate real-time characterization of lesions. The concept of the "optical biopsy" to predict polyp histology has gained prominence in recent years and may become clinically applicable with the advent of new imaging technology. This review aims to discuss current evidence and examine the emerging technologies as applied to the optical diagnosis of colorectal polyps. A structured literature search and review has been carried out of the evidence for diagnostic accuracy of image-enhanced endoscopy and emerging endoscopic imaging technologies. The image-enhanced endoscopy techniques are reviewed, including their basic scientific principles and current evidence for effectiveness. These include the established image-enhancement technologies such as narrow-band imaging, i-scan, and Fuji intelligent chromoendoscopy. More recent technologies including optical enhancement, blue laser imaging, and linked color imaging are discussed. Adjunctive imaging techniques in current clinical use are discussed, such as autofluorescence imaging and endocytoscopy. The emerging advanced imaging techniques are reviewed, including confocal laser endomicroscopy, optical coherence tomography, and Raman spectroscopy. Large studies of the established image-enhancement techniques show some role for the optical diagnosis of polyp histology, although results have been mixed, and at present only the technique of narrow-band imaging is appropriate for the diagnosis of low-risk polyps when used by an expert operator. Other image-enhancement techniques will require further study to validate their accuracy but show potential to support the use of a "resect-and-discard" approach to low-risk polyps. New technologies show exciting potential for real-time diagnosis, but further clinical studies in humans have yet to be performed.

Medical image enhancement in F-shift transformation domain.

Image enhancement technology plays an important role in the diagnosis and treatment of medical diseases. In this paper, we propose a method to automatically enhance medical images. The proposed method could be used to support clinical medical diagnosis, adjuvant therapy and curative effect diagnosis. This scheme uses contrast limited adaptive histogram equalization (CLAHE) method in F-shift transformation domain. Firstly, we adjust the overall brightness of the underexposed or overexposed image. Secondly, we perform CLAHE to enhance the low-frequency components obtained by one-level two-dimensional F-shift transformation (TDFS) on the adjusted images. At this stage, most of the coefficients in the high-frequency component can be changed to zero through properly setting the error bound. We then use inverse transformation to reconstruct image which is further enhanced with CLAHE. Compared to previous work, this approach takes into account not only the image enhancement, but also the data compression. Experimental results and comparison with state-of-the-art methods show that our proposed method has a better enhancement performance. Moreover, it has a certain data compression ability.

Object Detection on Underground Low-quality Images

Because of the insufficient illumination and dark environment, the underground image has little difference between the object and the background, and there will be irregular high-light spots, which will have a huge impact on the object detection. The commonly used methods for underground object detection are frame difference method, background difference, etc [1-3]. Both the frame difference method and the background difference method cannot accurately detect the object due to the influence of noise and image quality. This paper uses image enhancement technology to improve the quality of underground image, and proposes a comprehensive object detection method that is more robust to noise, and our algorithm can eliminate the influence of high-light point and improve the detection accuracy.

Quantitative nondestructive testing of wire rope based on pseudo-color image enhancement technology

ABSTRACTDetection of magnetic flux leakage (MFL) is widely used in nondestructive testing (NDT) of wire rope. We built and tested an MFL measuring device based on unsaturated magnetic excitation to address the problems of large volume, low accuracy and inconvenience of the traditional MFL instruments for locating and quantifying broken wires in wire ropes. We employed a pseudo-color imaging protocol to convert the MFL information to a pictorial representation. Localization of the breaks in the wire utilized the maximum modulus method. The color moment, statistical texture features, and spectral texture features were extracted from the image, revealing a network of broken wires, which could be localized and quantified. Testing showed that the device has high-detection accuracy and could detect small broken wires. Compared with traditional wire inspection devices, it has the advantages of small size, light weight and portability. We showed that pseudo-color image enhancement can effectively improve the recognition rate of broken wires and reduce the recognition error.

Quest for the best endoscopic imaging modality for computer-assisted colonic polyp staging.

BACKGROUNDIt was shown in previous studies that high definition endoscopy, high magnification endoscopy and image enhancement technologies, such as chromoendoscopy and digital chromoendoscopy [narrow-band imaging (NBI), i-Scan] facilitate the detection and classification of colonic polyps during endoscopic sessions. However, there are no comprehensive studies so far that analyze which endoscopic imaging modalities facilitate the automated classification of colonic polyps. In this work, we investigate the impact of endoscopic imaging modalities on the results of computer-assisted diagnosis systems for colonic polyp staging.AIMTo assess which endoscopic imaging modalities are best suited for the computer-assisted staging of colonic polyps.METHODSIn our experiments, we apply twelve state-of-the-art feature extraction methods for the classification of colonic polyps to five endoscopic image databases of colonic lesions. For this purpose, we employ a specifically designed experimental setup to avoid biases in the outcomes caused by differing numbers of images per image database. The image databases were obtained using different imaging modalities. Two databases were obtained by high-definition endoscopy in combination with i-Scan technology (one with chromoendoscopy and one without chromoendoscopy). Three databases were obtained by high-magnification endoscopy (two databases using narrow band imaging and one using chromoendoscopy). The lesions are categorized into non-neoplastic and neoplastic according to the histological diagnosis.RESULTSGenerally, it is feature-dependent which imaging modalities achieve high results and which do not. For the high-definition image databases, we achieved overall classification rates of up to 79.2% with chromoendoscopy and 88.9% without chromoendoscopy. In the case of the database obtained by high-magnification chromoendoscopy, the classification rates were up to 81.4%. For the combination of high-magnification endoscopy with NBI, results of up to 97.4% for one database and up to 84% for the other were achieved. Non-neoplastic lesions were classified more accurately in general than non-neoplastic lesions. It was shown that the image recording conditions highly affect the performance of automated diagnosis systems and partly contribute to a stronger effect on the staging results than the used imaging modality.CONCLUSIONChromoendoscopy has a negative impact on the results of the methods. NBI is better suited than chromoendoscopy. High-definition and high-magnification endoscopy are equally suited.

Foggy image enhancement technology based on improved Retinex algorithm

Foggy image enhancement technology based on improved Retinex algorithm

Linked color imaging improves detection of minimal change esophagitis in non-erosive reflux esophagitis patients.

Background and study aims Non-erosive reflux disease (NERD) includes minimal change esophagitis (MCE) and no endoscopic abnormalities. However, for most endoscopists, it is difficult to detect MCE with conventional white-light endoscopy (WLE). Linked color imaging (LCI) technology is the most recently developed image-enhancing technology and improves detection and differentiation of subtle mucosal changes using a color contrast method. This study assessed the efficacy of WLE combined with LCI for diagnosing MCE compared with WLE. Patients and methods Between February and May 2017, 44 NERD patients and 40 healthy subjects were enrolled in our study. First, the distal esophagus was examined using WLE followed by LCI. Second, three experienced endoscopists observed all the patients’ white-light (WL) images and corresponding images of WL and LCI and then recorded presence or absence of minimal change esophagitis (MCE +/–). The proportion of minimal change between the two groups was then compared. Third, five blinded endoscopists with different levels of endoscopic experience assessed whether MCE was present. Intraobserver reproducibility and interobserver agreement were described using the kappa value. Results The proportion of MCE in the NERD group (70.8 %, 35/48) was higher than that in the control group (22.5 %, 9/40, P < 0.001) when diagnosed by the three experienced endoscopists. Detection rates for MCE using WLE combined with LCI were higher than those using WLE (43/88, 48.9 % vs. 29/88, 33.0 %, P < 0.001). With WLE combined with LCI, intraobserver reproducibility significantly improved, indicating that the combined approach can improve interobserver agreement compared with using WLE alone. Conclusions Endoscopic diagnosis of MCE using WLE combined with LCI images is effective. Intraobserver reproducibility and interobserver agreement in MCE can be improved when LCI is applied with conventional imaging (Clinical trial registration number: NCT03068572).

Cracked Insulator Detection Based on R-FCN

The insulator is an important component of the high voltage transmission line. Inspecting the defects of the insulator plays an important role in ensuring the normal operation of the transmission line. The cracked insulator is the most common type of insulator defects. The traditional cracked insulator detection method is to use image denoising, single insulator disk segmentation, and the adjacent European distance to locate and identify cracked insulators. Experiments show that the robustness of these methods is poor and their ability to adapt to the environment is not good. Image denoising and image segmentation with the complex environment in images are very difficult tasks. Based on the extracted insulator strings, we change the problem to cracked insulators detection under complex conditions with deep learning. Region-based Fully Convolutional Networks (R-FCN), a deep learning algorithm, is adopted. Firstly, we apply image correction and cropping to images with different sample sizes and directions. Secondly, we use image enhancement technology to expand the dataset of samples. Finally, sliding window and coordinate mapping method are used when testing samples. The experimental results after the algorithm being applied to UVA dataset show an average accuracy rate of 90.5%. The inspection time of a single image is less than 1 second, and the model proposed in this paper has strong robustness and environmental adaptability.

Research on Underwater Image Enhancement Technology Based on Generative Adversative Net work s

Under the influence of light refraction and particle scattering, the underwater image has a low contrast and color attenuation . This paper proposes an underwater image enhancement method based on Generative Adversative Net work s (GANs). The main principle is to apply the GANs to learn the pixel transformation between images through con frontation training, so as to achieve the purpose of image enhancement. In this paper, we select the real underwater image as the data set and degrade it before inputting the image into the neural network. Then we input the corresponding degenerate image , so that the neural network can learn the mapping from input image to real image and realize the color reduction and enhancement of underwater image. Experiments show that this method has strong robustness and can be applied to underwater shooting and under water navigation.

Research and application of image enhancement technology in traffic scene of fog and haze

The images captured under fog and haze conditions, due to the scattering of the atmosphere, lead to blurred content and lower contrast, which brings serious impact on the application of traffic system and outdoor vision system. Based on the improved multi-scale Retinex algorithm MSR (Multi-Scale Retinex), a new fog removal method is proposed. This method estimates the luminance component according to the bootstrap filter, and then processes the image according to the adaptive histogram equalization method with limited contrast. Experiments show that the method can effectively remove the fog effect and realize the restoration of color degraded images.

Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring

Obtaining clear and true images is a basic requirement for agricultural monitoring. However, under the influence of fog, haze and other adverse weather conditions, captured images are usually blurred and distorted, resulting in the difficulty of target extraction. Traditional image dehazing methods based on image enhancement technology can cause the loss of image information and image distortion. In order to address the above-mentioned problems caused by traditional image dehazing methods, an improved image dehazing method based on dark channel prior (DCP) was proposed. By enhancing the brightness of the hazed image and processing the sky area, the dim and un-natural problems caused by traditional image dehazing algorithms were resolved. Ten different test groups were selected from different weather conditions to verify the effectiveness of the proposed algorithm, and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP method. Three image evaluation indicators including mean square error (MSE), peak signal to noise ratio (PSNR), and entropy were used to evaluate the dehazing performance. Results showed that the PSNR and entropy with the proposed method increased by 21.81% and 5.71%, and MSE decreased by 40.07% compared with the original DCP method. It performed much better than the histogram equalization dehazing method with an increase of PSNR by 38.95% and entropy by 2.04% and a decrease of MSE by 84.78%. The results from this study can provide a reference for agricultural field monitoring. Keywords: agricultural monitoring, image dehazing, monitoring image, dark channel prior (DCP), brightness promoting DOI: 10.25165/j.ijabe.20181102.3357 Citation: Wang X Y, Yang C H, Zhang J, Song H B. Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring. Int J Agric & Biol Eng, 2018; 11(2): 170–176.

1199 - CICERO: Cystoscopic imaging collection and enhancement project: A novel recording device and image enhancement technology

1199 - CICERO: Cystoscopic imaging collection and enhancement project: A novel recording device and image enhancement technology

Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring

Obtaining clear and true images is a basic requirement for agricultural monitoring. However, under the influence of fog, haze and other adverse weather conditions, captured images are usually blurred and distorted, resulting in the difficulty of target extraction. Traditional image dehazing methods based on image enhancement technology can cause the loss of image information and image distortion. In order to address the above-mentioned problems caused by traditional image dehazing methods, an improved image dehazing method based on dark channel prior (DCP) was proposed. By enhancing the brightness of the hazed image and processing the sky area, the dim and un-natural problems caused by traditional image dehazing algorithms were resolved. Ten different test groups were selected from different weather conditions to verify the effectiveness of the proposed algorithm, and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP method. Three image evaluation indicators including mean square error (MSE), peak signal to noise ratio (PSNR), and entropy were used to evaluate the dehazing performance. Results showed that the PSNR and entropy with the proposed method increased by 21.81% and 5.71%, and MSE decreased by 40.07% compared with the original DCP method. It performed much better than the histogram equalization dehazing method with an increase of PSNR by 38.95% and entropy by 2.04% and a decrease of MSE by 84.78%. The results from this study can provide a reference for agricultural field monitoring. Keywords: agricultural monitoring, image dehazing, monitoring image, dark channel prior (DCP), brightness promoting DOI: 10.25165/j.ijabe.20181102.3357 Citation: Wang X Y, Yang C H, Zhang J, Song H B. Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring. Int J Agric & Biol Eng, 2018; 11(2): 170–176.

Diagnostic accuracy of i-scan image enhancement for real-time endoscopic diagnosis of small colorectal polyps: a meta-analysis.

Objective:i-Scan is a digital image enhancement technology, reported to improve diagnostic performance during endoscopy. Previous studies have investigated the accuracy of i-scan for distinguishing between neoplastic and non-neoplastic colonic polyps and suggested diagnostic accuracy close to that required for use in routine clinical practice. The aim of this study was to perform a meta-analysis of the available literature investigating diagnostic accuracy for i-scan optical diagnosis when made in real time for colorectal polyps <10 mm in size.Data sources:The databases of Medline, Embase and the Cochrane Library were searched for relevant studies.Methods:A bivariate random effects model was used to produce pooled sensitivity and specificity values, and univariable meta-regression was applied for subgroup analysis.Results:The meta-analysis included 15 studies, and diagnosis of 2817 polyps. The area under the hierarchical summary receiver-operating characteristic curve was 0.96. The pooled sensitivity of i-scan optical diagnosis was 0.92 (95% confidence interval 0.85–0.95). The specificity was 0.90 (95% confidence interval 0.83–0.94).Conclusions:This meta-analysis updates and summarizes the available evidence concerning the diagnostic performance of i-scan for small colorectal polyps. An acceptable degree of accuracy was found, showing potential to support a ‘resect and discard’ strategy for colonic polyps. There was not found to be a significant difference in diagnostic performance between different clinical settings (i.e. teaching hospitals or general hospitals).

The Application of Improved Histogram Equalization and Image Segmentation in Satellite Image

Reasonable image enhancement and image segmentation technologies help people to identify the object from original images, which cannot be easily recognized by naked eyes. A method of image recognition based on improved histogram equalization and image segmentation was proposed in this paper. By stretching the histogram in a custom range and excluding singular points, contrast of images was enhanced with more detail information. Then, feature vector in different color space was used to segment the object from original images. From testing, this method was proved to be efficient, simple and convenient to implement and can be used in real time system because of its simplicity.

Novel Imaging Technologies in Laparoscopic Gynecologic Surgery: A Systematic Review

Novel imaging technologies continued to be introduced into the operative setting. In particular, novel image-enhanced laparoscopic techniques are being explored for use in gynecologic operations. This systematic review describes these technologies in four relevant areas of gynecologic surgery. The PubMed database was searched for human, English-language studies, and the reference lists of retrieved articles were reviewed. An analysis of pooled data from 34 studies that met inclusion criteria was performed. The results suggest that image-enhanced technology may be useful in several common gynecologic procedures. Auto- and drug-enhanced fluorescence laparoscopy allow for increased detection of nonpigmented endometriotic lesions. Using these technologies for peritoneal staging of ovarian malignancy is of uncertain benefit. Drug-enhanced fluorescence laparoscopy for sentinel lymph node (SLN) detection in patients with uterine or cervical malignancy is feasible, showing a high rate of SLN detection, but a low sensitivity of identifying metastases. Finally, their use in intra-operative visualization of the ureter is promising. The majority of available data was from feasibility studies with limited sample sizes. Nevertheless, the results described in this systematic review support the expectation that these upcoming image-enhanced laparoscopy techniques will play a more important role in the future care of gynecologic patients.

Minimal Esophageal Lesions Detected by Digital Chromoendoscopy Using Optical Enhancement SystemTM Associated to High Definition Plus Optical Magnification in Non-Erosive Reflux Disease (NERD)

Introduction: Patients with GERD have an increased number, dilation and tortuosity of microscopic intra-papillary capillary loops (IPCLs). Recently, an image-enhanced technology called Optical Enhancement system (OE systemTM) is equipped in Pentax video processor EPK-i7010. It improves visualization of microvessels. Also new high definition scopes with optical magnification (MagniviewTM) were developed (increase image >136 times). AIM: To evaluate diagnosis ability of OE systemTM +MagniviewTM scopes detecting minimal esophageal lesions IPCLs and predicting reflux in non-erosive reflux disease (NERD).Figure: Red ROC curve: pH-Impedanciometry. Blue ROC curve: Biopsy. UELS: Upper endoscopy low segment. UEMS: Upper endoscopy middle segment. UEUP: Upper endoscopy upper segment. UEG: Upper endoscopy in general.Table: Table. Contingency Table comparing Upper Endoscopy with Ph-Impedanciometry and histopathological BiopsyMethods: Prospective, non-randomized study. Patients enrolled had GERD symptoms. If esophagus was normal at endoscopy, pH-impedanciometry was performed to discriminate NERD patients (NG) from non reflux disease (Control group, CG). OE systemTM+MagniviewTM endoscopy was performed. Images were recorded and examined blindly by 3 endoscopists. Biopsies from each esophagus level were done. Images were compared to histology to predict inflammation. Inclusion Criteria: >18 years-old, >8 points at GERD questionnaire), abcense of Barrett's esophagus or erosive sign (Los Angeles classification grade A-D) by endoscopy (by neither white light nor I-ScanTM). Exclusion Criteria: History of esophagitis, achalasia, esophageal varices, esophageal cancer, PPI or NSAIDs consumption, coagulopathy, gastric lesions, gastroparesis, esophageal/gastric surgery, pregnancy. Results: 57 patients were analyzed [36 (63.2%) NG, 21 (36.9%) CG] [mean age 48 yo, 46 women (81%)]. Main symptom was regurgitation in 27 cases (47.3%). IPCLs was observed in 94.4% of NG, being lower third the most affected (P<0.05). In CG 8 patients (38%) had IPCLs (P<0.05 for NG). IPCLs dilatation was most common find (91.6%). Unlike IPCLs tortuosity, both dilatation and increased number of IPCLs showed a significant difference between groups. Also both lesions were more common (P<0.05) between patients with inflammation at histology. OE systemTM+Magniview scopes ability predicting GERD (gold standard: pH-Impedanciometry) reached sensitivity, specificity, PPV, NPV and accuracy of 94.4, 61.9, 91.4, 81.8 and 86.67% respectively. Lower third of esophagus had higher sensibility and accuracy (88.9 and 87.7%). Kappa inter and intra-observer value was 0.85 and 0.90 respectively. Conclusion: OE systemTM+MagniviewTM scopes can detect IPCLs and predict NERD with high sensitivity and accuracy.

Researching on the Adaptability Technology of Substation Inspection Robot to the Substation’s Complex Environments

The four arms crawler substation inspection robot system was developed, using the integrated navigation technology including laser and odometer, then the robot can run free without the limitation of the road environment and the fixed track; using the image enhancement technology in the complicated environment, improve automatically detecting identification accuracy of the substation equipment status in the bad environment, so the substation inspection robot can implement inspection in all area of the transformer substation, as well as has environment adaptability, engineering construction convenience and the higher promotion application value.