Showing papers on "Edge enhancement published in 2012"

Enhancement and noise reduction of very low light level images

Abstract: A general method for image contrast enhancement and noise reduction is proposed in this paper. The method is developed especially for enhancing images acquired under very low light conditions where the features of images are nearly invisible and the noise is serious. By applying an improved and effective image de-haze algorithm to the inverted input image, the intensity can be amplified so that the dark areas become bright and the contrast get enhanced. Then, the joint-bilateral filter with the original green component as the edge image is introduced to suppress the noise. Experimental results validate the performance of the proposed approach.

Content-Aware Dark Image Enhancement Through Channel Division

Abstract: The current contrast enhancement algorithms occasionally result in artifacts, overenhancement, and unnatural effects in the processed images. These drawbacks increase for images taken under poor illumination conditions. In this paper, we propose a content-aware algorithm that enhances dark images, sharpens edges, reveals details in textured regions, and preserves the smoothness of flat regions. The algorithm produces an ad hoc transformation for each image, adapting the mapping functions to each image's characteristics to produce the maximum enhancement. We analyze the contrast of the image in the boundary and textured regions, and group the information with common characteristics. These groups model the relations within the image, from which we extract the transformation functions. The results are then adaptively mixed, by considering the human vision system characteristics, to boost the details in the image. Results show that the algorithm can automatically process a wide range of images—e.g., mixed shadow and bright areas, outdoor and indoor lighting, and face images—without introducing artifacts, which is an improvement over many existing methods.

A rank ordered filter for medical image edge enhancement and detection using intuitionistic fuzzy set

Abstract: This paper gives a novel scheme using intuitionistic fuzzy set theory to enhance the edges of medical images. Medical images contain lots of uncertainties, as they are poorly illuminated and fuzzy/vague in nature. So, direct segmentation techniques will not produce better results. There are lots of researches on edge enhancement starting from non-fuzzy to fuzzy set, but proper enhancement (highlighting important structures) is not obtained. Enhancement of edges helps in recovering the important structures that are not visible properly. Even minute pathological blood vessels/cells are not visible properly and in that case edge enhancement will enhance these blood vessels/cells. Intuitionistic fuzzy set theory is found suitable in medical image processing as it considers more (two) uncertainties as compared to fuzzy set theory. In the processing phase, image is initially converted to intuitionistic fuzzy image and intuitionistic fuzzy entropy is used to obtain the optimum value of the parameter in the membership and non-membership functions. Then it computes the total variation of the pixels with respect to the median value of the image window (rank order filtering). This enhances the borders or the edges of the image. The resulting image is then segmented (edge detected) using standard Canny's edge detector, when simply using Canny's edge detector does not give better result. From the result it is observed that on comparing with non-fuzzy and fuzzy methods, the proposed method gives better information about the images, which is helpful to the pathologists in accurate diagnosing of diseases.

Selective edge enhancement in three-dimensional vortex imaging with incoherent light.

Abstract: We demonstrate a new imaging method enabling a selective edge contrast enhancement of three-dimensional amplitude objects with spatially incoherent light. The imaging process is achieved in a spiral modification of Fresnel incoherent correlation holography and uses a vortex impulse response function. The correlation recordings of the object are acquired in a one-way interferometer with the wavefront division carried out by a spatial light modulator. Two different methods based on applying a helical reference wave in the hologram recording and a digital spiral phase modulation in image reconstruction are proposed for edge enhancement of amplitude objects. Results of both isotropic and anisotropic spiral imaging are demonstrated in experiments using an LED as an incoherent source of light.

A Fast Dynamic Range Compression With Local Contrast Preservation Algorithm and Its Application to Real-Time Video Enhancement

Abstract: This study addresses low dynamic range (LDR) image/video enhancement for digital video cameras. A new fast dynamic range compression format with a local-contrast-preservation (FDRCLCP) algorithm resolves this problem efficiently. The proposed FDRCLCP algorithm can combine with any continuously differentiable intensity transfer function to achieve LDR image enhancement. In combination with the FDRCLCP algorithm, a new intensity-transfer function is proposed, adaptively accomplishing dynamic range compression and edge-contrast enhancement depending on the local mean value of the input luminance image. The proposed method also extends to a linear color remapping approach, not only preserving the original image's color information, but also controlling color saturation of the resulting image. Moreover, a look-up-table (LUT) acceleration approach improves the processing speed of the proposed FDRCLCP algorithm in processing video signals, allowing real-time video enhancement processing. Experimental results show that the proposed method not only provides good visual representation in both quantitative and visual comparisons, but also achieves real-time performance for video processing.

Image contrast enhancement using classified virtual exposure image fusion

Abstract: In our daily life, digital cameras and smart phones have been widely used to take pictures However, digital cameras and smart phones have a limited dynamic range, which is much lower than that human eyes can perceive Thus, the photographs taken in high dynamic range scenes often exhibit under-exposure or over-exposure artifacts in shadow or highlight regions In this study, an image fusion based approach, called classified virtual exposure image fusion (CVEIF), is proposed for image enhancement First, a function imitating the F-stop concept in photography is designed to generate several virtual images having different intensity Then, a classified image fusion method, which blends pixels in distinct luminance classes using different fusion functions, is proposed to produce a fused image in which every image region is well exposed Experimental results on four different kinds of generic images, including a normal image, a low-contrast images, a backlight image, and a dark scene image, have shown that the proposed CVEIF approach produced more pleasingly enhanced images than other methods

Image Data Compression Method, Pattern Model Positioning Method in Image Processing, Image Processing Apparatus, Image Processing Program, and Computer Readable Recording Medium

Abstract: There is provided a data compression method for increasing a reduction ratio, while keeping a sufficient characteristic amount, to seek speeding up of processing, the method being for compressing image data in pattern model positioning in image processing of searching out of an image to be searched and positioning a pattern model corresponding to a pre-registered image. The method includes the steps of: computing an edge strength image having edge strength information and an edge angle image having edge angle information with respect to each pixel constituting an image; transforming the edge angle image of each pixel into an edge angle bit image expressed by an edge angle bit indicating an angle with a pre-defined fixed width; and compressing the edge angle bit image to create an edge angle bit reduced image by taking a sum with respect to each edge angle bit.

Image enhancement methods

Abstract: Methods of image enhancement are disclosed. In one aspect, the method of image enhancement is for use with an image capture device, such as a security document reader, for the attenuation, separation or reduction of reflections from objects, such as security documents.

Spectral Recognition Techniques and MLC of IRS P6 LISS III image for Coastal Landforms Extraction along South West Coast of Tamilnadu, India

Abstract: The Remote sensing technology is measured or observed reflected energy to construct an image of the landscape beneath the platform passage in a discrete pixel format. The geometric and radiometric characteristics of remotely sensed image provide information about earth's surface. In the present study, the primary data product obtained from IRS P6 satellite LISS - III images (23.5 m) are used to extract the landforms the South West coast of Tamil nadu, India. The study area comprises different types of landforms in nature. The selected image processing techniques are employed such as, geometric correction, radiometric correction for removal of atmospheric errors and noise from image and to identify spectral and spatial variations in structure, texture, pattern of objects in the image. Here, the spectral recognition statistics namely edge detection; edge enhancement; histogram equalization, principal component analysis and maximum likelihood classifier algorithms are applied for demarcate the coastal landforms. In the maximum likelihood classification process, the spectral properties (Digital Number) of an object (kn) in the image has been identified using mean and covariance of pixels in training set, then the probability function (Px) determines the distribution of that group of pixels (class) in the image. The coastal landforms are segmented as separate class from the image based on their spectral and spatial characteristics such as shoreline, beach, sand dunes, erosional and accretion lands, water body, river deltas, and manmade infrastructure with attribute of shape, area, location and spatial distribution.

A 37 × 37 pixels artificial retina chip with edge enhancement function for 3-D stacked fully implantable retinal prosthesis

Abstract: To restore visual sensation of blind patients suffering from age-related macular degeneration (AMD) and retinitis pigmentosa (RP), we have been developing a fully implantable retinal prosthesis with three-dimensional (3-D) stacked artificial retina chip. This chip has layered structure similar to human retina and includes such functions as converting incident light into electrical signal, processing visual information, and generating stimulus current pulse. In this paper, we have developed a photoreceptor chip with selectable sensitivity circuit and a stimulus current generator chip with edge enhancement function using four-neighbor Laplacian filter for 3-D staked artificial retina chip. As results, the 37 × 37 pixels photoreceptor chip correctly converted incident light into electrical signal, and the edge enhancement and stimulus current generator chip with edge enhancement function successfully emphasized the edges of input images and generated biphasic stimulus current patterns. By stacking these two chips, artificial retina chip having a pixel number of 37 × 37, a pixel size of 75 × 75 μm2, and the fill-factor more than 27%, will be realized.

Fractional differential algorithms for rock fracture images

Abstract: It is a new research topic that fractional differential theory is used into image processing. This paper presents a new type of algorithms to improve the fractional differential Tiansi operator, which can significantly enhance the edge detection result. The studied algorithms are based on the enhancement ability of fractional differential to image details, and they can be used to analyse the properties of fractional differential. The general procedure of the algorithms is as follows: firstly, Tiansi template is divided into eight sub-templates with different directions around the detecting pixel, and then the eight weight sum values for the eight sub-templates are obtained. Furthermore, those eight weights are classified into different groups. In this way, the three improved algorithms with different enhancing ranges are obtained. Finally, the experiments of edge detection show that the improved algorithms can obtain edge information more effectively and can show much more detailed information tha...

Halo artifacts reduction method for variational based realtime retinex image enhancement

Abstract: In this paper, we propose a novel halo reduction method for variational based Retinex image enhancement. In variational based Retinex image enhancement, a cost function is designed based on the illumination characteristics. The enhanced image is obtained by extracting the illumination component, which gives minimum cost, from the given input image. Although this approach gives good enhancement quality with less computational cost, a problem that dark regions near edges remain dark after image enhancement, known as halo artifact, still exists. In order to suppress such artifacts effectively, the proposed method adaptively adjusts the parameter of the cost function, which influences the trade-off relation between reducing halo artifacts and preserving image contrast. The proposed method is applicable to an existing realtime Retinex image enhancement hardware implementation.

Intensity-based gain adaptive unsharp masking for image contrast enhancement

Abstract: With the increasing application of image processing techniques in various areas, contrast enhancement still remains as a core pre-processing element that critically affects the performance of subsequent procedures. Among the available enhancement methods, unsharp masking is one of the efficient approaches that could produce satisfactory results on a wide range of digital images. However, properly setting the unsharp masking algorithmic parameters is a challenging task. In this work, a gain adaptive scheme is proposed where enhancements are focused on the mid-range intensity regions. This approach effectively makes use of a Gaussian kernel to adjust the enhancement gain and reduces the probability of over-range artefact occurrences. Experiments are conducted on a collection of public available digital colour images and in-house captured real world images. Satisfactory test results have illustrated the performance of the proposed method.

An improved Unsharp Masking algorithm for enhancement of mammographic masses

Abstract: Unsharp Masking (UM) is a classical and popular tool for sharpening in digital images. Mammographic masses with diameters less than 1 cm are generally benign, while those with diameters above 2 cm could prove cancerous and hence require further investigation. This paper introduces two new filter templates to catalyze the performance of UM algorithm using an improved high pass filter. The conventional UM algorithm is extremely sensitive to noise because of the presence of the linear high pass filter, on the other hand region segmentation does not prove effective when dealing with the objects having multiple discontinuities. The proposed algorithm combines the conventional UM algorithm with the region segmentation approach. Simulation results show a marked improvement in performance in terms of contrast and edge enhancement as compared to the recently published works in literature. Moreover the results are also subjectively evaluated by medical experts which validates the effectiveness of the proposed technique.

Image Enhancement and Its Various Techniques

Abstract: Image enhancement is the task of applying certain alterations to an input image like as to obtain a more visually pleasing image. The alteration usually requires interpretation and feedback from a human evaluator of the output resulting image. Image enhancement is to improve the image quality so that the resultant image is better than the original image for a specific application or set of objectives. Enhancement techniques such as alpha rooting operate on the transform domain. The transform domain enables operation on the frequency content of the image, and therefore high frequency content such as edges and other subtle information can easily be enhanced. However, these techniques bring about tonal changes in the images and can also generate unwanted artifacts in many cases, as it is not possible to enhance all parts of the image in balanced manner.

Method for image fusion based on principal component analysis

Abstract: The present invention yields with a method to fusion absorption, differential phase contrast and dark-field (scattering) signals obtained with X-ray phase contrast sensitive techniques, such as an arrangement of gratings. The new method fuses the absorption and dark-field signals by principal component analysis (PCA); further the differential phase contrast is merged into the PCA fused image to obtain edge enhancement effect. Due to its general applicability and its simplicity in usage, the suggested invention is expected to become a standard method for image fusion scheme using phase contrast imaging, in particular on medical scanners (for instance mammography), inspection at industrial production lines, non-destructive testing, and homeland security.

Image processing method for boundary resolution enhancement

Abstract: An image processing method for boundary resolution enhancement is disclosed. Firstly, an image is transferred into an image layer. Noise of the image layer is removed by a bilateral filter and crisp edges are retained at the same time. Moreover, the image layer is interpolated by an interpolation filter for resolution enhancement. The image processing method of the present invention can lower the image blur degree substantially, enhance the image resolution and be widely implemented in all sorts of image/video processing hardware devices.

Method and device for reducing noise in ultrasound image

Abstract: The invention relates to an image processing technique, particularly relates to an image data processing technique in an ultrasound image, and more particularly to a method and a device for reducing noise in the ultrasound image The method provided by the invention comprises the following steps: reading ultrasound image data; selecting an adjacent region with each pixel point as a center; computing the variance mean value ratio of the pixel points in each direction in the adjacent regions; computing discrimination factors according to the variance mean value ratio; respectively distinguishing the adjacent regions of the pixel points as an edge region, a non-edge region and a semi-edge region according to the discrimination factors; respectively carrying out filtering processing on the different edge regions; and outputting the processed ultrasound image data According to the technical scheme provided by the invention, the edge enhancement and spot suppression of images can be simultaneously realized; and the method provided by the invention has the advantages of simple algorithm, self adaptation, strong practicality and the like, is easy to achieve by hardware, and can be conducted in real time

Regularization function for video super-resolution using auxiliary high resolution still images

Abstract: In this paper video super-resolution using sequences generated by dual-mode cameras is studied Dual-mode cameras are capable to shoot high-resolution still images at a low rate while taking low-resolution video from the scene An algorithm for video super-resolution is proposed which uses high-resolution still images generated by the dual-mode cameras for super-resolution process We use high-resolution still images to form a regularization function and use it for the de-blurring stage of the super-resolution process The simulation results show that performance of the proposed algorithm is superior in terms of recovering high-frequency details of the original video and edge reconstruction

Combining depth information and local edge detection for stereo image enhancement

Abstract: The recent developments in 3D display technology have opened new horizons and have raised a number of challenges related to the processing and coding of 3D media. Today, stereoscopic image technology is becoming widely used in many fields. The physical limitations of image acquisition systems, however, make stereoscopic technology far from being the most widely accepted solution. Furthermore, the depth/disparity extreme ranges may subject the viewers' eyes to additional strain, causing more discomfort. To address this issue, we propose in this paper to improve stereoscopic image quality by a novel contrast enhancement method that combines local edges and depth information. The contrast is increased locally, at specific depth levels for left and right views. The increase of contrast is controlled based on the depth information, and aims at promoting the nearest objects in the 3D scene. The results obtained from a psychophysical experiment are encouraging and show that the proposed method produces stereo images that are less stressful on the eyes, thus providing more pleasant viewing experience.

Enhancement of large faults with a windowed 3D Radon transform filter

Abstract: Summary One of the hard problems in seismic imaging is to make good images of faults based on seismic of varying quality. Large faults may run through stratigraphic layers where seismic image quality can vary from excellent to almost chaotic. When trying to image such faults by the use of seismic attributes it is not uncommon to only capture fragments that are associated with decent quality in the seismic image. Here a windowed 3D Radon based filter is applied as an edge enhancement step on top of edge detection with a seismic attribute in order to improve the signal strength of large faults. Furthermore it is demonstrated that subsequent ant tracking results are of significantly better quality when comparing workflows with and without the enhancement step. Thus imaging of large faults can significantly benefit from applying a windowed 3D Radon filter.

Adaptive rank based impulsive noise reduction in color images

Abstract: In this paper a new approach to the problem of impulsive noise removal in color images is presented. The novel design can be seen as a generalization of the vector median filter and is based on the weighting of the dissimilarity measures between pixels contained in the local filtering window. The weights assigned to each color sample are decreasing functions of their ranks in an ordered sequence, while the ordering is based on the distance between a given pixel and its neighbors. In this way, the rank weights diminish the influence of outliers on the proposed filter output. Extensive experiments revealed that the new filtering design efficiently suppresses the impulsive noise corrupting color images while enhancing their edges. This unique feature can be utilized in any application in which noise removal combined with edge enhancement is desired.

Analysis of Distance Measurement System of Leading Vehicle

Abstract: Based on the digital image processing theory, a new method of measuring the leading vehicle distance was proposed. The input image using the method of edge enhancement and morphological transformation was established, so the edges of objects were enhanced to identify. The target vehicle was identified and calibrated in the image by using the method of the obstacle detection by segmentation and decision tree. The relationship between coordinates value in image space and the data of the real space plane was established by applying the ray angles. Thus, through accessing to image pixel coordinates of the vehicle, the vehicle's actual position in the plane can be calculated. At last, the leading vehicle distance based on the calculating model of inverse perspective mapping was measured. By using software VC++, an experiment program was made. The experiment results prove that the method of measuring the leading vehicle distance is simple and effective. It can meet the requirement of intelligent vehicle technologies. It is an more available and more advanced method to calculate the leading vehicle distance. The target vehicle was identified and calibrated in the image by using the method of the obstacle detection by segmentation and decision tree. The relationship between coordinates value in image space and the data of the real space.

Malaysian vehicle license plate recognition using double edge detection

Abstract: Vehicle plate number is a unique combination of characters and numbers. Hence, it has been used in various application as personal identification such as for parking system identification, security monitoring system and etc. This paper illustrated the double edge detection technique in order to enhance the vehicle plate image, before character recognition process. Firstly, the vehicle image is captured, and then it will be re-sized and cropped until the resolution of image is 300×300. After the re-sized process, First Edge detection is applied to the image. Threshold of black and white are 59 and 60 respectively used to change the image into black and white colour only. Next, Second Edge detection is used to remove the unwanted image and only remain the plate number in white colour. MATLAB software is used in this experiment.

A Hybrid Method for Edge Continuity Based on Pixel Neighbors Pattern Analysis (PNPA) for Remote Sensing Satellite Images

Abstract: Edge enhancement is derived from lack of accurate result from edge detection techniques. The image which is captured from long distances carries a lot of noise and blur which causes edge discontinuity. Although some novel algorithms which are based on cellular neural network, fuzzy enhancement and binary morphology have shown accuracy in order to obtain refined edge but still the problem of edge discontinuity arises. Eliminating discontinuity of edge a hybrid technique is proposed based on pixel neighbors pattern analysis PNPA. In the technique Canny operator for initial edge detection, PNPA operation for edge enhancement are performed for remote sensing satellite image successively. The visual and subjective evaluation shows that the proposed PNPA operation can effectively eliminate the influence of edge discontinuity which occurred due to noise and blurr in original captured image, as comparing to existing edge segmenting processes.

Edge detection of SAR images using incorporate shift-invariant DWT and binarization method

Abstract: In this paper, a fast unsupervised noise-robustness edge detection method in the synthetic aperture radar (SAR) images is presented. Within this method the whole edge detection process is divided into two simple tasks. Firstly, the input image is decomposed with shift-invariant discrete wavelet transform (DWT) and edge enhancement is done by the information of the subbands of the SAR images. Secondly, a fast unsupervised hybrid method of the binarization and edge detection is used for SAR images edge detection. This method incorporates speckle reduction and edge detection as a single process so that complex operations are avoided. Compared to the denoising first and then edge detection method, simulations show the proposed method achieves better performance and has low computations.