High-resolution Color Images Research Articles

고해상도 컬러 영상 워핑의 실시간 구현을 위한 영상 캐시 알고리즘

This paper presents a new image cache algorithm for real-time implementation of high-resolution color image warping. The cache memory is divided into four cache memory modules for simultaneous readout of four input image pixels in consideration of the color filter array (CFA) pattern of an image sensor and CFA image warping. In addition, a pipeline structure from the cache memory to an interpolator is shown to guarantee the generation of an output image pixel at each system clock cycle. The proposed image cache algorithm is applied to an FPGA-based real-time color image warping, and experimental results are presented to show the validity of the proposed method.

Laser induced single spot oxidation of titanium

Titanium oxides have a wide range of applications in industry, and they can be formed on pure titanium using different methods. Laser-induced oxidation is one of the most reliable methods due to its controllability and selectivity. Colour marking is one of the main applications of the oxidation process. However, the colourizing process based on laser scanning strategies is limited by the relative large processing area in comparison to the beam size. Single spot oxidation of titanium substrates is proposed in this research in order to increase the resolution of the processed area and also to address the requirements of potential new applications. The method is applied to produce oxide films with different thicknesses and hence colours on titanium substrates. High resolution colour image is imprinted on a sheet of pure titanium by converting its pixels’ colours into laser parameter settings. Optical and morphological periodic surface structures are also produced by an array of oxide spots and then analysed. Two colours have been coded into one field and the dependencies of the reflected colours on incident and azimuthal angles of the light are discussed. The findings are of interest to a range of application areas, as they can be used to imprint optical devices such as diffusers and Fresnel lenses on metallic surfaces as well as for colour marking.

EFFECT OF PANSHARPENED IMAGE ON SOME OF PIXEL BASED AND OBJECT BASED CLASSIFICATION ACCURACY

Classification is the most important method to determine type of crop contained in a region for agricultural planning. There are two types of the classification. First is pixel based and the other is object based classification method. While pixel based classification methods are based on the information in each pixel, object based classification method is based on objects or image objects that formed by the combination of information from a set of similar pixels. Multispectral image contains a higher degree of spectral resolution than a panchromatic image. Panchromatic image have a higher spatial resolution than a multispectral image. Pan sharpening is a process of merging high spatial resolution panchromatic and high spectral resolution multispectral imagery to create a single high resolution color image. The aim of the study was to compare the potential classification accuracy provided by pan sharpened image. In this study, SPOT 5 image was used dated April 2013. 5m panchromatic image and 10m multispectral image are pan sharpened. Four different classification methods were investigated: maximum likelihood, decision tree, support vector machine at the pixel level and object based classification methods. SPOT 5 pan sharpened image was used to classification sun flowers and corn in a study site located at Kadirli region on Osmaniye in Turkey. The effects of pan sharpened image on classification results were also examined. Accuracy assessment showed that the object based classification resulted in the better overall accuracy values than the others. The results that indicate that these classification methods can be used for identifying sun flower and corn and estimating crop areas.

EFFECT OF PANSHARPENED IMAGE ON SOME OF PIXEL BASED AND OBJECT BASED CLASSIFICATION ACCURACY

Abstract. Classification is the most important method to determine type of crop contained in a region for agricultural planning. There are two types of the classification. First is pixel based and the other is object based classification method. While pixel based classification methods are based on the information in each pixel, object based classification method is based on objects or image objects that formed by the combination of information from a set of similar pixels. Multispectral image contains a higher degree of spectral resolution than a panchromatic image. Panchromatic image have a higher spatial resolution than a multispectral image. Pan sharpening is a process of merging high spatial resolution panchromatic and high spectral resolution multispectral imagery to create a single high resolution color image. The aim of the study was to compare the potential classification accuracy provided by pan sharpened image. In this study, SPOT 5 image was used dated April 2013. 5m panchromatic image and 10m multispectral image are pan sharpened. Four different classification methods were investigated: maximum likelihood, decision tree, support vector machine at the pixel level and object based classification methods. SPOT 5 pan sharpened image was used to classification sun flowers and corn in a study site located at Kadirli region on Osmaniye in Turkey. The effects of pan sharpened image on classification results were also examined. Accuracy assessment showed that the object based classification resulted in the better overall accuracy values than the others. The results that indicate that these classification methods can be used for identifying sun flower and corn and estimating crop areas.

REMOTE SENSING IMAGE FUSION USING ICA AND OPTIMIZED WAVELET TRANSFORM

Abstract. In remote-sensing image processing, fusion (pan-sharpening) is a process of merging high-resolution panchromatic and lower resolution multispectral (MS) imagery to create a single high-resolution color image. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. However, the pan-sharpening image produced by these methods gets the high color distortion of spectral information. In this paper, to minimize the spectral distortion we propose a remote sensing image fusion method which combines the Independent Component Analysis (ICA) and optimization wavelet transform. The proposed method is based on selection of multiscale components obtained after the ICA of images on the base of their wavelet decomposition and formation of linear forms detailing coefficients of the wavelet decomposition of images brightness distributions by spectral channels with iteratively adjusted weights. These coefficients are determined as a result of solving an optimization problem for the criterion of maximization of information entropy of the synthesized images formed by means of wavelet reconstruction. Further, reconstruction of the images of spectral channels is done by the reverse wavelet transform and formation of the resulting image by superposition of the obtained images. To verify the validity, the new proposed method is compared with several techniques using WorldView-2 satellite data in subjective and objective aspects. In experiments we demonstrated that our scheme provides good spectral quality and efficiency. Spectral and spatial quality metrics in terms of RASE, RMSE, CC, ERGAS and SSIM are used in our experiments. These synthesized MS images differ by showing a better contrast and clarity on the boundaries of the "object of interest - the background". The results show that the proposed approach performs better than some compared methods according to the performance metrics.

REMOTE SENSING IMAGE FUSION USING ICA AND OPTIMIZED WAVELET TRANSFORM

In remote-sensing image processing, fusion (pan-sharpening) is a process of merging high-resolution panchromatic and lower resolution multispectral (MS) imagery to create a single high-resolution color image. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. However, the pan-sharpening image produced by these methods gets the high color distortion of spectral information. In this paper, to minimize the spectral distortion we propose a remote sensing image fusion method which combines the Independent Component Analysis (ICA) and optimization wavelet transform. The proposed method is based on selection of multiscale components obtained after the ICA of images on the base of their wavelet decomposition and formation of linear forms detailing coefficients of the wavelet decomposition of images brightness distributions by spectral channels with iteratively adjusted weights. These coefficients are determined as a result of solving an optimization problem for the criterion of maximization of information entropy of the synthesized images formed by means of wavelet reconstruction. Further, reconstruction of the images of spectral channels is done by the reverse wavelet transform and formation of the resulting image by superposition of the obtained images. To verify the validity, the new proposed method is compared with several techniques using WorldView-2 satellite data in subjective and objective aspects. In experiments we demonstrated that our scheme provides good spectral quality and efficiency. Spectral and spatial quality metrics in terms of RASE, RMSE, CC, ERGAS and SSIM are used in our experiments. These synthesized MS images differ by showing a better contrast and clarity on the boundaries of the "object of interest - the background". The results show that the proposed approach performs better than some compared methods according to the performance metrics.

A Manifold Alignment Approach for Hyperspectral Image Visualization With Natural Color

The trichromatic visualization of hundreds of bands in a hyperspectral image (HSI) has been an active research topic. The visualized image shall convey as much information as possible from the original data and facilitate easy image interpretation. However, most existing methods display HSIs in false color, which contradicts with user experience and expectation. In this paper, we propose a new framework for visualizing an HSI with natural color by the fusion of an HSI and a high-resolution color image via manifold alignment. Manifold alignment projects several data sets to a shared embedding space where the matching points between them are pairwise aligned. The embedding space bridges the gap between the high-dimensional spectral space of the HSI and the RGB space of the color image, making it possible to transfer natural color and spatial information in the color image to the HSI. In this way, a visualized image with natural color distribution and fine spatial details can be generated. Another advantage of the proposed method is its flexible data setting for various scenarios. As our approach only needs to search a limited number of matching pixel pairs that present the same object, the HSI and the color image can be captured from the same or semantically similar sites. Moreover, the learned projection function from the hyperspectral data space to the RGB space can be directly applied to other HSIs acquired by the same sensor to achieve a quick overview. Our method is also able to visualize user-specified bands as natural color images, which is very helpful for users to scan bands of interest.

Extended Guided Filtering for Depth Map Upsampling

The authors address the problem of depth map upsampling using a corresponding high-resolution color image. The depth map is captured by low-resolution time-of-flight cameras paired with a high-resolution RGB camera. Inspired by guided image filtering, the proposed method not only uses the structure of the high-resolution color image as guidance, it also exploits local gradient information of depth images to suppress potential texture-copying artifacts. In addition, the authors introduce onion-peel-order filtering that predicts depth values from outside inward in a concentric-layer order, which avoids depth bleeding during the propagation process. Quantitative and qualitative experimental results demonstrate the effectiveness and robustness of this approach over prior depth map upsampling methods.

In situ optical measurements of Chang'E-3 landing site in Mare Imbrium: 2. Photometric properties of the regolith

The panorama cameras onboard the Yutu Rover of the Chang'E-3 lunar mission acquired hundreds of high-resolution color images of the lunar surface and captured the first in situ lunar opposition effect (OE) since the Apollo era. We extracted the phase curve and the color ratio in three bands with the phase angle range from 2 degrees to 141 degrees. Photometric inversions using the Hapke model reveal that submicroscopic dusts are present in the landing area and both the coherent backscattering and the shadow hiding are responsible for the strong OE. Compared with spaceborne measurements, the grains in the landing site are brighter, more transparent, and appear to be better crystallized than the average maria basaltic grains. The results show that the phase-reddening effect appears to be present in the in situ phase curves. The current phase curve can be used as the ground-truth validations of any future spaceborne phase curve measurement over the landing site region.

Real-time implementation of an adaptive simultaneous dynamic range compression and local contrast enhancement algorithm on a GPU

Dynamic range compression is a fundamental function required in digital video cameras and display monitors to improve the visual appeal of color images. This paper presents a real-time implementation of an algorithm for adaptive dynamic range compression and contrast enhancement on a graphics processing unit (GPU). To achieve this, we first designed an adaptive intensity transfer function to handle the enhancement of standard-dynamic-range (8 bits/channel) images with both low- and high-intensity. The proposed algorithm then combines the intensity transfer function with an existing simultaneous dynamic range compression and local contrast enhancement (SDRCLCE) algorithm for the simultaneous compression of dynamic range and enhancement of local contrast. To reach real-time performance in processing high-resolution color images, the proposed algorithm is implemented using an NVIDIA GeForce GTX 650 Ti GPU based on NVIDIA’s Compute Unified Device Architecture (CUDA) parallel programming running on a 3.33 GHz Intel Core i5-661 CPU. Compared with a LUT-accelerated implementation, the proposed GPU implementation was shown to accelerate the processing of 1024 × 1024 color images by 11.1 times and color images of 4096 × 4096 pixels by 7.5 times, including the cost of memory copy between the host and device.

Optical Coherence Tomography Angiography and En Face Optical Coherence Tomography Features of Paracentral Acute Middle Maculopathy

To characterize the optical coherence tomography (OCT) angiography, en face OCT, and microperimetry features of paracentral acute middle maculopathy in both the acute phase and after resolution, and to propose a classification of distinct subtypes of this entity. Retrospective observational case series. Clinical histories, high-resolution digital color imaging, spectral-domain OCT images, fluorescein angiography, OCT angiography images, and en face OCT images of 16 patients with paracentral acute middle maculopathy were evaluated. Microperimetry was available in 6 patients. The most common referring diagnoses were isolated branch retinal arterial occlusion (5/16), combined central retinal vein and cilioretinal artery occlusion (4/16), and isolated central retinal vein occlusion (4/16). All patients demonstrated hyperreflective plaque-like lesions at the level of the inner nuclear layer on spectral-domain OCT, with no fluorescein angiographic correlate. OCT angiography demonstrated variable areas of capillary dropout within the superficial and deep retinal capillary plexi in these areas. En face OCT highlighted confluent areas of middle retina hyperreflectivity corresponding to these lesions. Three distinct en face OCT patterns were observed: arteriolar, fern-like, and globular. Microperimetry demonstrated relative scotomas mapping to the area of middle retinal hyperreflectivity seen on en face OCT. Paracentral acute middle maculopathy may be best evaluated with the use of en face OCT imaging, which corresponds to subjective and objective visual field defects. En face OCT appearance may be used to classify paracentral acute maculopathy into distinct subtypes.

Customized Orthogonal Locality Preserving Projections With Soft-Margin Maximization for Face Recognition

Face recognition still is a challenging task since face images may be affected by changes in the scene, such as in head pose, face expression, or illumination. In addition, face pattern representation often requires several dimensions, which poses additional challenges for face recognition. We propose a novel face recognition method based on projections of high-dimensional face image representations into lower dimensionality and highly discriminative spaces. This is achieved by a modified orthogonal locality preserving projection (OLPP) method that uses a customized locality definition scheme to preserve the face class structure in the lower dimensionality face feature space. The proposed method can work with sparse and dense face image representations (i.e., it can use subsets or all face image pixels) and tends to be robust to data outliers and noise. Besides, we introduce a sparse representation using interpolated landmarks, designed to preserve important details in high-resolution color face images (e.g., eyes), and compensate for uncertainties in landmark positioning during face image feature extraction. The face images are classified in this lower dimensionality feature space using a trained soft-margin support vector machine, so it performs better than the nearest neighbor rule used in the typical OLPP method. A set of experiments was designed to evaluate the proposed scheme under various conditions found in practice (such as changes in head pose, face expression, illumination, and in the presence of occlusion artifacts). The experimental results were obtained using five challenging public face databases (namely, Poznan University of Technology, Fundacao Educacional Inaciana, Facial Recognition Technology, Yale, and Our Database of Faces). These experiments suggest that our sparse representation for high-resolution face color images, integrated to the proposed lower dimensionality feature space and classification scheme, tends to obtain higher accuracy values than those obtained using typical sparse and dense representations for the same face images in grayscale. To evaluate the generality of our lower dimensionality feature space and classification scheme, additional tests using full low-resolution grayscale face images were performed, as often used in face recognition (e.g., typical OLPP method). Our experiments suggest that the proposed approach can also provide higher accuracy values than comparable state-of-the-art methods available in the literature when using full low-resolution grayscale face images (i.e., dense representations).

Time-of-Flight Sensor Calibration for a Color and Depth Camera Pair.

We present a calibration method of a time-of-flight (ToF) sensor and a color camera pair to align the 3D measurements with the color image correctly. We have designed a 2.5D pattern board with irregularly placed holes to be accurately detected from low resolution depth images of a ToF camera as well as from high resolution color images. In order to improve the accuracy of the 3D measurements of a ToF camera, we propose to perform ray correction and range bias correction. We reset the transformation of the ToF sensor which transforms the radial distance into the scene depth in Cartesian coordinate through ray correction. Then we capture a planar scene from different depths to correct the distance error that is shown to be dependent not only on the distance but also on the pixel location. The range error profiles along the calibrated distance are classified according to their wiggling shapes and each cluster of profiles with similar shape are separately estimated using a B-spline function. The standard deviation of the remaining random noise is recorded as an uncertainty information of distance measurements. We show the performance of our calibration method quantitatively and qualitatively on various datasets, and validate the impact of our method by demonstrating an RGB-D shape refinement application.

A Super-Resolution Image Reconstruction using Natural Neighbor Interpolation

A super-resolution image reconstruction al- gorithm using natural neighbor interpolation is proposed and its performance is evaluated. The algorithm is di- vided into two stages: image registration and the recon- struction of a high-resolution color image. In the first stage, as shifts between images are usually unknown, the algorithm computes an approximation of these dis- placements by solving the system of linear equations proposed by Keren, Peleg, and Brada, then the pixels of all low-resolution images are mapped into a high- resolution grid by computing the new coordinates using the motion vectors. In the second stage, the pixel val- ues that match the high-resolution grid are interpolated using natural neighbor interpolation which is a weighted average interpolation method for scattered data, based in the areas of the Voronoi polygons of the neighboring pixels. Finally, the proposed natural neighbor super- resolution algorithm is compared with some popular super-resolution algorithms presented in literature.

The atlas of mouse development eHistology resource.

The Atlas of Mouse Development by Professor Mathew Kaufman is an essential text for understanding mouse developmental anatomy. This definitive and authoritative atlas is still in production and is essential for any biologist working with the mouse embryo, although the last revision dates back to 1994. Here, we announce the eHistology online resource that provides free access to high-resolution colour images digitized from the original histological sections (www.emouseatlas.org/emap/eHistology/index.php) used by Kaufman for the Atlas. The images are provided with the original annotations and plate numbering of the paper atlas and enable viewing the material to cellular resolution.

High quality multi-spectral and panchromatic image fusion technologies based on Curvelet transform

Multi-spectral and panchromatic image fusion is one of the most common method of synthesis of high-resolution color image. High quality color image is needed to fuse panchromatic images of high spatial resolution and keep the spectral characteristics of multi spectral image. This algorithm integrates the features of IHS transform, which can preserve the spectral characteristics, and Curvelet transform, which can better maintain the edge. Based on the experiments on proposed algorithms, the comparison used the five image fusion evaluation standard is given among the IHS method, the wavelet method, the PCA method and the Curvelet transform used in this text. The synthetic image, image information entropy, relatively non dimensional global error (ERGAS) and universal image quality index UIQI all better than other images in three bands, and the average gradient is optimal in two bands.

DHMAPI: A Knowledge-Based System for Identification of Medicinal and Aromatic Plants

About 43,000 plant species are said to exist in India of which 7,500 plant species are referred to in Indian folklore. The recent era has witnessed revitalisation in traditional health care system, and more people have started depending upon traditional herbal health care systems for various ailments. Majority of these medicinal plants are collected from the wild. Authentic identification/recognition of medicinal and aromatic plant species is a major challenge since there are a large number of vernacular names for the same species as well as different botanical species have the same common name. Hence, a knowledge-based system namely digital herbarium of medicinal and aromatic plants in India (DHMAPI) has been developed to help farmers and extension workers to identify medicinal and aromatic plants. The system is supported by a database containing information of about 350 medicinal and aromatic plant species and their colour images. High-resolution colour images of medicinal and aromatic plants were captured from different parts of India and integrated with technical information of respective species. Based on these herbaria information, a web-based system was developed and launched (http://www.dmapr.org.in:8080/dhmap/Home.jsp). The system, called DHMAPI, is a knowledge system that helps users to compare their unknown species with the existing dataset to identify their species.

Developing the eHistology Atlas.

The eMouseAtlas project has undertaken to generate a new resource providing access to high-resolution colour images of the slides used in the renowned textbook ‘The Atlas of Mouse Development’ by Matthew H. Kaufman. The original histology slides were digitized, and the associated anatomy annotations captured for display in the new resource. These annotations were assigned to objects in the standard reference anatomy ontology, allowing the eHistology resource to be linked to other data resources including the Edinburgh Mouse Atlas Gene-Expression database (EMAGE) an the Mouse Genome Informatics (MGI) gene-expression database (GXD). The provision of the eHistology Atlas resource was assisted greatly by the expertise of the eMouseAtlas project in delivering large image datasets within a web environment, using IIP3D technology. This technology also permits future extensions to the resource through the addition of further layers of data and annotations to the resource.Database URL: www.emouseatlas.org/emap/eHistology/index.php

Lossless Quality Steganographic Color Image Compression

This paper develops a steganography-based paradigm for lossless-quality compression of high-resolution color images acquired by megapixel cameras. Our scheme combines space-domain and frequency-domain image processing operations where in the space domain, color-brightness separation is exploited, and in the frequency domain, spectral properties of the Fourier magnitude and phase of the color image is exploited. Working in both domains concurrently allows for an approach to ultrahigh-resolution image compression that addresses both issues of quality and storage size. Experimental results as well as empirical observations show that our technique exceeds the highest quality JPEG image compression standard in terms of compression rates while being very competitive with JPEG in the overall fidelity of the decompressed image, with the added advantage of being able to recover the original fine details in the color image without any degradations common in lossy image compression techniques.

Depth map upsampling using compressive sensing based model

We propose a new method to enhance the lateral resolution of depth maps with registered high-resolution color images. Inspired by the theory of compressive sensing (CS), we formulate the upsampling task as a sparse signal recovery problem that solves an underdetermined system. With a reference color image, the low-resolution depth map is converted into suitable sampling data (measurements). The signal recovery problem, defined in a constrained optimization framework, can be efficiently solved by variable splitting and alternating minimization. Experimental results demonstrate the effectiveness of our CS-based method: it competes favorably with other state-of-the-art methods with large upsampling factors and noisy depth inputs.