Improving the Sharpness of Digital Images Using a Modified Laplacian Sharpening Technique

Many imaging systems produce images with deficient sharpness due to different real limitations. Hence, various image sharpening techniques have been used to improve the acutance of digital images. One of such is the well-known Laplacian sharpening technique. When implementing the basic Laplacian technique for image sharpening, two main drawbacks were detected. First, the amount of introduced sharpness cannot be increased or decreased. Second, in many situations, the resulted image suffers from a noticeable increase in brightness around the sharpened edges. In this article, an improved version of the basic Laplacian technique is proposed, wherein it contains two key modifications of weighting the Laplace operator to control the introduced sharpness and tweaking the second order derivatives to provide adequate brightness for recovered edges. To perform reliable experiments, only real-degraded images were used, and their accuracies were measured using a specialized no-reference image quality assessment metric. From the obtained experimental results, it is evident that the proposed technique outperformed the comparable techniques in terms of recorded accuracy and visual appearance

Fuzzy Rule Based Enhancement in the SMRT Domain for Low Contrast Images

AbstractFuzzy techniques offer a new and flexible framework for the development of image enhancement algorithms. They are nonlinear, knowledge-based and robust. The potentials of fuzzy set theory for image enhancement are still not investigated in comparison with other established methodologies. In this paper, an examination of fuzzy methods in transform domain is considered. Fuzzy rule based contrast enhancement in the Sequency based Mapped RealTransform (SMRT) domain for block level processing is explored. SMRT, being an integer transform,is computationally efficient and the fuzzy rule based technique is applied to the entire blocks in the transform domain

Evaluation of Perceptual Contrast and Sharpness Measures for Meteorological Satellite Images

AbstractSharpness and contrast have great impact on perceived quality of an image. This paper focuses on sharpness and contrast measures to evaluate quality of Thermal Infrared (TIR1) channel of Indian National Satellite-3D (INSAT-3D) without using any reference image. Most of the sharpness metrics can scarcely manage to discern image quality degradation against high frequency behavior due to noise. Six Image Quality Measures (IQMs) are employed to study their behavior in terms of blur, noise and intensity changes simultaneously. Results show that (1) change in value of Measure Of Enhancement By Entropy (EMEE) is more discernible with change in contrast of an INSAT-3D image as compared to other measures and (2) Second Derivative Like Measure Of Enhancement (SDME) has a potential to distinguish high frequency content due to sharpness arisen due to un estimated noise up to some remarkable level in case of TIR1 INSAT-3D satellite images. Performance comparison of six measures against blur, noise, contrast and sharpness changes is presented

Pre-processing Technique for Wireless Capsule Endoscopy Image Enhancement

Wireless capsule endoscopy (WCE) is used to examine human digestive tract in order to detect abnormal area. However, it has been a challenging task to detect abnormal area such as bleeding due to poor quality and dark images of WCE. In this paper, pre-processing technique is introduced to ease classification of the bleeding area. Anisotropic contrast diffusion method is employed in our pre-processing technique as a contrast enhancement of the images. There is a drawback to the method proposed B. Li in which the quality of WCE image is degraded when the number of iteration increases. To solve this problem, variance is employed in our proposed method. To further enhance WCE image, Discrete Cosine Transform is used with anisotropic contrast diffusion. Experimental results show that both proposed contrast enhancement algorithm and sharpening WCE image algorithm provide better performance compared with B. Li’s algorithm since SDME and EBCM value is stable whenever number of iterations increases, and sharpness measurement using gradient and PSNR are both improved by 31.5% and 20.3% respectively

Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets

In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pix- els. We have proposed a novel image enhancement scheme based on intuitionistic hesi- tant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the pho- tographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.Web of Science500866

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

Deep learning based deep-sea automatic image enhancement and animal species classification

The automatic classification of marine species based on images is a challenging task for which multiple solutions have been increasingly provided in the past two decades. Oceans are complex ecosystems, difficult to access, and often the images obtained are of low quality. In such cases, animal classification becomes tedious. Therefore, it is often necessary to apply enhancement or pre-processing techniques to the images, before applying classification algorithms. In this work, we propose an image enhancement and classification pipeline that allows automated processing of images from benthic moving platforms. Deep-sea (870 m depth) fauna was targeted in footage taken by the crawler “Wally” (an Internet Operated Vehicle), within the Ocean Network Canada (ONC) area of Barkley Canyon (Vancouver, BC; Canada). The image enhancement process consists mainly of a convolutional residual network, capable of generating enhanced images from a set of raw images. The images generated by the trained convolutional residual network obtained high values in metrics for underwater imagery assessment such as UIQM (~ 2.585) and UCIQE (2.406). The highest SSIM and PSNR values were also obtained when compared to the original dataset. The entire process has shown good classification results on an independent test data set, with an accuracy value of 66.44% and an Area Under the ROC Curve (AUROC) value of 82.91%, which were subsequently improved to 79.44% and 88.64% for accuracy and AUROC respectively. These results obtained with the enhanced images are quite promising and superior to those obtained with the non-enhanced datasets, paving the strategy for the on-board real-time processing of crawler imaging, and outperforming those published in previous papers.This work was developed at Deusto Seidor S.A. (01015, Vitoria-Gasteiz, Spain) within the framework of the Tecnoterra (ICM-CSIC/UPC) and the following project activities: ARIM (Autonomous Robotic sea-floor Infrastructure for benthopelagic Monitoring); MarTERA ERA-Net Cofund; Centro para el Desarrollo Tecnológico Industrial, CDTI; and RESBIO (TEC2017-87861-R; Ministerio de Ciencia, Innovación y Universidades). This work was supported by the Centro para el Desarrollo Tecnológico Industrial (CDTI) (Grant No. EXP 00108707 / SERA-20181020)

Using Contextual Learning to Improve Diagnostic Accuracy: Application in Breast Cancer Screening

Abstract-Clinicians need to routinely make management decisions about patients who are at risk for a disease such as breast cancer. This paper presents a novel clinical decision support system that is capable of helping physicians make diagnostic decisions. We apply this support system to improve the specificity of breast cancer screening. The system utilizes clinical context (e.g., demographics, medical history) to minimize the false positive rates while avoiding false negatives. An online contextual learning algorithm is used to update the diagnostic strategy presented to the physicians over time. We analytically evaluate the diagnostic performance loss of the proposed algorithm, in which the true patient distribution is not known and needs to be learned, as compared with the optimal strategy where all information is assumed known, and prove that the false positive rate of the proposed learning algorithm asymptotically converges to the optimum. Moreover, the relevancy of each contextual information is assessed, enabling the approach to identify specific contexts that provide the most value of information in reducing diagnostic errors. Experiments were conducted using patient data collected from a large academic medical center. Our proposed approach outperforms the current clinical practice by 36% in terms of false positive rate given a 2% false negative rate. Index Terms-Breast cancer, cancer screening, computer-aided diagnosis system, online learning, contextual learning, multiarmed bandit

Near-Infrared Forearm Vascular Width Calculation Using Radius Estimation of Tangent Circle

Peer reviewe