Image enhancement for digital radiography

Once in a digital form, a radiographic image may be processed in several ways in order to turn the visualization an act of improved diagnostic value. Practitioners should be aware that, depending on each clinical context, digital image processing techniques are available to help to unveil visual information that is, in fact, carried by the bare digital radiograph and may be otherwise neglected. The range of visual enhancement procedures includes simple techniques that deal with the usual brightness and contrast manipulation up to much more elaborate multi-scale processing that provides customized control over the emphasis given to the relevant finer anatomical details. This chapter is intended to give the reader a practical understanding of image enhancement techniques that might be helpful to improve the visual quality of the digital radiographs and thus to contribute to a more reliable and assertive reporting

Processing of multispectral thermal IR data for geologic applications

Multispectral thermal IR data were acquired with a 24-channel scanner flown in an aircraft over the E. Tintic Utah mining district. These digital image data required extensive computer processing in order to put the information into a format useful for a geologic photointerpreter. Simple enhancement procedures were not sufficient to reveal the total information content because the data were highly correlated in all channels. The data were shown to be dominated by temperature variations across the scene, while the much more subtle spectral variations between the different rock types were of interest. The image processing techniques employed to analyze these data are described

Reflectance Transformation Imaging (RTI) System for Ancient Documentary Artefacts

This tutorial summarises our uses of reflectance transformation imaging in archaeological contexts. It introduces the UK AHRC funded project reflectance Transformation Imaging for Anciant Documentary Artefacts and demonstrates imaging methodologies

Measure of image enhancement by parameter controlled histogram distribution using color image

Abstract: Histogram Equalization (HE) technique is simple and effectively used for contrast enhancement. But HE is not suitable for consumer electronic products because it may produces washed out appearance image. The new quantitative measures of image enhancement and frequency domain based methods used for object detection and visualization and enhancement technique driven by both global and local process on luminance and chrominance components of the image. This measure of image enhancement is related with the concepts of Weber's law of the human visual systems. It helps to choose the best parameters and transform. This approach based on parameter controlled histogram distribution method can enhance simultaneously overall contrast and sharpness of an image. This approach also increases the visibility of specified portions or better maintaining image color. This analysis provides better performance for contrast enhancement Index terms: Histogram equalization, Image enhancement, Contrast enhancement, I.INTRODUCTION A digital image is converted into analogy signal which is scanned into a display. Before the processing image is converted into digital format, Digitization includes sampling of image and quantization of sampled values. After converting the image into bit, information is processed. In image processing, images are available in digitized form that is arrays of finite length binary format. For digitization, the given images are sampled on a discrete grid and each sample or pixel is quantized using a finite number of bits. The digitized image is used in computer. Image enhancement which transforms digital images to enhance the visual information. To enhance image contrast is the intensity mapping that reassigns the intensity of pixels through a monotonically increasing function. Primary operation for all vision and image processing tasks in several areas. In forensic video/image analysis tasks surveillance videos have quite different qualities compared with other videos such as the videos for high quality entertainment or TV broadcasting. Enhancement transformation to modify the contrast of an image within a display's dynamic range is therefore required in order to show full information content in the videos. Contrast enhancement is an important function in image processing applications. The objective of this method is to make an image clearly recognized for a specific application. Point operation based enhancement techniques are contrast stretching, non-linear point transformation, histogram modeling. The non-linearity is introduced by many imaging lighting device which can be described with a point operation. Gamma correction is using the power law's light intensity operation [1] which is adjusting the lightness/darkness level of their prints. X=(x) (1/gamma value) Where x is the original pixel value. Depending on the gamma value, image can be lightened or darkened. so, it will improve visual contrast and also decreases the visual contrast too. Histogram Equalization (HE) is most popular technique for contrast enhancement. HE makes uniform distribution of the gray level for an image. But, consumer electronics such as Flat panel display (FPD), HE is rarely applied in directly because the significant changes in brightness [2]. The HE effectiveness is depends on the contrast of the original image. In general, HE will flatten out the probability distribution of an image and increase its dynamic range and also will make the average brightness towards the middle gray level of an image regardless of the input image, and produce the objectionable artifacts and unnatural contrast effect. This makes the visual quality of processed image is unsatisfactory. Surveillance videos have quietly different qualities compared with other videos such as videos for high quality entertainment or T

Design of Novel Algorithm and Architecture for Gaussian Based Color Image Enhancement System for Real Time Applications

This paper presents the development of a new algorithm for Gaussian based color image enhancement system. The algorithm has been designed into architecture suitable for FPGA/ASIC implementation. The color image enhancement is achieved by first convolving an original image with a Gaussian kernel since Gaussian distribution is a point spread function which smoothen the image. Further, logarithm-domain processing and gain/offset corrections are employed in order to enhance and translate pixels into the display range of 0 to 255. The proposed algorithm not only provides better dynamic range compression and color rendition effect but also achieves color constancy in an image. The design exploits high degrees of pipelining and parallel processing to achieve real time performance. The design has been realized by RTL compliant Verilog coding and fits into a single FPGA with a gate count utilization of 321,804. The proposed method is implemented using Xilinx Virtex-II Pro XC2VP40-7FF1148 FPGA device and is capable of processing high resolution color motion pictures of sizes of up to 1600x1200 pixels at the real time video rate of 116 frames per second. This shows that the proposed design would work for not only still images but also for high resolution video sequences.Comment: 15 pages, 15 figure

SLIC Based Digital Image Enlargement

Low resolution image enhancement is a classical computer vision problem. Selecting the best method to reconstruct an image to a higher resolution with the limited data available in the low-resolution image is quite a challenge. A major drawback from the existing enlargement techniques is the introduction of color bleeding while interpolating pixels over the edges that separate distinct colors in an image. The color bleeding causes to accentuate the edges with new colors as a result of blending multiple colors over adjacent regions. This paper proposes a novel approach to mitigate the color bleeding by segmenting the homogeneous color regions of the image using Simple Linear Iterative Clustering (SLIC) and applying a higher order interpolation technique separately on the isolated segments. The interpolation at the boundaries of each of the isolated segments is handled by using a morphological operation. The approach is evaluated by comparing against several frequently used image enlargement methods such as bilinear and bicubic interpolation by means of Peak Signal-to-Noise-Ratio (PSNR) value. The results obtained exhibit that the proposed method outperforms the baseline methods by means of PSNR and also mitigates the color bleeding at the edges which improves the overall appearance.Comment: 6 page

An optimal factor analysis approach to improve the wavelet-based image resolution enhancement techniques

The existing wavelet-based image resolution enhancement techniques have many assumptions, such as limitation of the way to generate low-resolution images and the selection of wavelet functions, which limits their applications in different fields. This paper initially identifies the factors that effectively affect the performance of these techniques and quantitatively evaluates the impact of the existing assumptions. An approach called Optimal Factor Analysis employing the genetic algorithm is then introduced to increase the applicability and fidelity of the existing methods. Moreover, a new Figure of Merit is proposed to assist the selection of parameters and better measure the overall performance. The experimental results show that the proposed approach improves the performance of the selected image resolution enhancement methods and has potential to be extended to other methods

Medical image enhancement using threshold decomposition driven adaptive morphological filter

One of the most common degradations in medical images is their poor contrast quality. This suggests the use of contrast enhancement methods as an attempt to modify the intensity distribution of the image. In this paper, a new edge detected morphological filter is proposed to sharpen digital medical images. This is done by detecting the positions of the edges and then applying a class of morphological filtering. Motivated by the success of threshold decomposition, gradientbased operators are used to detect the locations of the edges. A morphological filter is used to sharpen these detected edges. Experimental results demonstrate that the detected edge deblurring filter improved the visibility and perceptibility of various embedded structures in digital medical images. Moreover, the performance of the proposed filter is superior to that of other sharpener-type filters