Image Enhancement Operations Research Articles

Image Enhancement and Feature Extraction Based on Low-Resolution Satellite Data

The purpose of this study is to investigate the sensitivity of contrast-based textural measurements and morphological characteristics that derive from high-resolution satellite imagery (three-band SPOT-5) when diverse image enhancements techniques are piloted. The general framework of the application is the built-up/nonbuilt-up detection. In the existence of a low-resolution reference layer, we apply supervised learning that indirectly reduces the uncertainty and improves the quality of the reference layer. Based on the new class label assignments, the image histogram is adjusted suitably for the computation of contrast-based textural/morphological features. A case study is presented where we test a mixture of image enhancement operations like linear and decorrelation stretching and assess the performance through ROC analysis against available building footprints. Experimental results demonstrate that spectral band combination is the key factor that conditions the contrast of grayscale images. Contrast adjustment (before or after the band combination and merging) supports considerably the extraction of informative features from a low-contrast image; in case of a well-contrasted image, the improvement is marginal.

Image Enhancement in Encrypted Domain over Cloud

Cloud-based multimedia systems are becoming increasingly common. These systems offer not only storage facility, but also high-end computing infrastructure which can be used to process data for various analysis tasks ranging from low-level data quality enhancement to high-level activity and behavior identification operations. However, cloud data centers, being third party servers, are often prone to information leakage, raising security and privacy concerns. In this article, we present a Shamir's secret sharing based method to enhance the quality of encrypted image data over cloud. Using the proposed method we show that several image enhancement operations such as noise removal, antialiasing, edge and contrast enhancement, and dehazing can be performed in encrypted domain with near-zero loss in accuracy and minimal computation and data overhead. Moreover, the proposed method is proven to be information theoretically secure.

Statistical Analysis of Resulting Palm vein Image through Enhancement Operations

Nowadays biometric is playing a key role in the field of forensic and commercial applications. The vein biometrics is a robust biometric in recent trends. The vein pattern is very difficult to forge or fake. The traits are not going to be changed from birth to death. This paper discusses image enhancement operations and its result when applied on multispectral palm vein image. The image enhancement operations are much helpful to extract the vein pattern as features. The experiments can be used to highlight or trace a vein pattern lies at palm region of hand. The proposed work gains vein pattern and considered as the stepping stone towards feature extraction. The paper also discusses the comparison of statistical properties such as mean, standard deviation and coefficient of original image and images resulting out through enhancement operations. The enhancement operation is a key to gain the vein pattern. The image analysis can be framed well using statistical image measurements.

Image Retrieval Based on Content Using Color Feature

Content-based image retrieval from large resources has become an area of wide interest in many applications. In this paper we present a CBIR system that uses Ranklet Transform and the color feature as a visual feature to represent the images. Ranklet Transform is proposed as a preprocessing step to make the image invariant to rotation and any image enhancement operations. To speed up the retrieval time, images are clustered according to their features using k-means clustering algorithm.

Example-based image color and tone style enhancement

Color and tone adjustments are among the most frequent image enhancement operations. We define a color and tone style as a set of explicit or implicit rules governing color and tone adjustments. Our goal in this paper is to learn implicit color and tone adjustment rules from examples. That is, given a set of examples, each of which is a pair of corresponding images before and after adjustments, we would like to discover the underlying mathematical relationships optimally connecting the color and tone of corresponding pixels in all image pairs. We formally define tone and color adjustment rules as mappings, and propose to approximate complicated spatially varying nonlinear mappings in a piecewise manner. The reason behind this is that a very complicated mapping can still be locally approximated with a low-order polynomial model. Parameters within such low-order models are trained using data extracted from example image pairs. We successfully apply our framework in two scenarios, low-quality photo enhancement by transferring the style of a high-end camera, and photo enhancement using styles learned from photographers and designers.

Adaptive Fuzzy Enhancement Algorithm of Surface Image based on Local Discrimination via Grey Entropy

This paper used the value of grey entropy in the neighborhood window as parameters to measure the level of current pixel being edge point, which can be used to be the basis of image enhancement operation. Simulation results show that the algorithm can effectively improve the quality of surface image, and it supplied a good approach to surface image processing compared with other methods.

Enhanced Segmentation Procedure for Intima-Adventitial Layers of Common Carotid Artery

This paper presents an enhanced Segmentation technique for use on noisy B-mode ultrasound images of the carotid artery. This method is based on Image Enhancement, Edge detection and Morphological operations in boundary detection. This procedure may simplify the job of the practitioner for analyzing accuracy and variability of segmentation results. Possible plaque regions are also highlighted. A thorough evaluation of the method in the clinical environment shows that inter observer variability is evidently decreased and so is the overall analysis time. The results demonstrate that it has the potential to perform qualitatively better than applying existing methods in intima and adventitial layer detection on B-mode images.

Extração automatica de segmentos lineares de rodovias em imagens de resoluções variadas

In this paper a methodology for automatic extraction of road segments from images with different resolutions (low, middle and high resolution) is presented. It is based on a generalized concept of lines in digital images, by which lines can be described by the centerlines of two parallel edges. In the specific case of low resolution images, where roads are manifested as entities of 1 or 2 pixels wide, the proposed methodology combines an automatic image enhancement operation with the following strategies: automatic selection of the hysteresis thresholds and the Gaussian scale factor; line length thresholding; and polygonization. In medium and high resolution images roads manifest as narrow and elongated ribbons and, consequently, the extraction goal becomes the road centerlines. In this case, it is not necessary to apply the previous enhancement step used to enhance roads in low resolution images. The results obtained in the experimental evaluation satisfied all criteria established for the efficient extraction of road segments from different resolution images, providing satisfactory results in a completely automatic way.

Genetic algorithms for optimal image enhancement

Genetic algorithms represent a class of highly parallel adaptive search processes for solving a wide range of optimization and machine learning problems. The present work is an attempt to demonstrate their adaptivity and effectiveness for searching global optimal solutions in selecting an appropriate image enhancement operator automatically.

Capture, Digitization, and Enhancement of Videotape Frames for Industrial Hygiene Applications

Abstract Documentation of industrial hygiene surveys and studies is sometimes performed by videotaping. Incorporation of the video images into digital files and performing useful operations on these digitized images is now possible using MS-DOS personal computer systems. This article evaluates the setup of such a system, describes several image enhancement operations offered by this system, and looks at different applications in which the system may be useful to the industrial hygienist.

A note on parametric image enhancement

A method for defining image enhancement operators based upon a parametric family is considered. This family of operators can be used for either context-free or context-sensitive enhancement that can be used either locally or globally. A modification of this procedure is suggested whereby the estimation of the parameters is performed using statistics which allow for efficient use of cellular or neighborhood image processors rather than those computed using the usual arithmetic operations.