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Super-resolution (SR) reconstruction produces one or a series of high-resolution images from a series of low-resolution images. In this paper, we apply the regularization-based SR image reconstruction method on the basis of multi-frame image SR. Fisrstly, a linear observation model is utilized to associate the recorded LR images with the unknown reconstructed HR image estimates, and we apply the bilateral...
In this paper, a novel method of improving dynamic range of imaging device is proposed. Unlike multi-capture HDR methods, the suggested method requires only a small modification to existing CMOS imaging sensor. By changing exposure time line by line, one can obtain a multi-exposure image in a single capture of an image. With such captured image, digital image processing algorithms are applied to recover...
Unlike PAN sharpening, the fusion of SAR with multispectral data involve use of non-overlapping spectral bands which poses certain inconsistencies viz. 1) radiometric differences due to their acquisition in entirely different spectral bands 2) geometric differences due to range and angular imaging of SAR and Optical sensors respectively. Apart from these, speckle noise and registration related factors...
According to the imaging mechanism of remote sensing image, together with interferences caused by atmosphere, there are always some noises in the image. Therefore, denoising methods are always needed in remote sensing applications. However, denoising method could only remove exterior noises in the remote sensing image, while there also many intrinsic confusions and fluctuations exist in it, which...
The real noise model corrupting the observed images is unknown and usually random statistical model. Consequently, classical SRR (Super Resolution Reconstruction) algorithms using median (L1) and mean (L2) filtering structures may degrade the reconstructed image sequence rather than enhance it. The mathematical analysis [1] demonstrates that the meridian filtering structure exhibits more robust characteristic...
Super-resolution is the task of creating an high resolution image from a low resolution input sequence. To overcome the difficulties of fine image registration, several methods have been proposed exploiting the non-local intuition, i.e. any datapoint can contribute to the final result if it is relevant. These algorithms however limit in practice the search region for relevant points in order to lower...
In this paper a new, faster approach which is different from all the other conventional image vectorization techniques. Using canny edge detection we are able to find the sharp edges in the image and the assigning shades to each identifiable segment using random colour extraction from the original image. Finally mapping the colour blobs with the SVG Schema and generating a scalable vector image. This...
The CMOS image sensors are achieving a growing presence in today's mobile applications as the industry acknowledges the advances of the CMOS-based technology and its scaling possibilities. The roadmap recently unveiled for CMOS Image Sensor is announcing ever smaller pixels, after 1.4μm pixel pitch, demos with a pitch of 1.1μm were presented, and it also announces the future generation of pixels with...
Knife Edge Scanning Microscopy (KESM) is a high-throughput imaging technique used to obtain large-scale anatomical information (≈1cm3) at sub-micrometer resolution. Data acquisition has been fully automated, however significant post-processing and reconstruction must be done manually. KESM is unique in that illumination and tissue sectioning are performed using a diamond knife. Therefore many of the...
In this paper we introduce a new algorithm for reconstruction of low-dose CT images. The approach, called multi-resolution feature fusion (MRFF), combines the textural qualities of conventional filtered-back projection images, with the noise suppression ability of non-quadratic regularized iterative reconstructions, to form a fast image reconstruction with good noise texture properties. Low-dose abdominal...
Clinical magnetic resonance imaging (MRI) data is normally corrupted by random noise from the measurement process which reduces the accuracy and reliability of any automatic analysis. For this reason, denoising methods are often applied to increase the : Signal-to-Noise Ratio (SNR) and improve image quality. The search for efficient image denoising methods is still a valid challenge at the crossing...
Low-noise CMOS image sensors (CIS) employing column-parallel amplifiers that significantly reduce temporal noise, as well as electron-multiplication CCD (EM-CCD) image sensors are becoming popular for very-low-light-level imaging. This paper presents a column-parallel ADC for CMOS imagers using a successive operation of folding-integration ADC (FI-ADC) and cyclic ADC for attaining very low noise,...
Finiteness of data causes spectral leakage and degradation of spectral resolution in the transformed domain. A set of methods known under the name of `non linear apodization techniques' have been used to reduce the spectral leakage. Similarly, there are different super-resolution techniques exist to enhance the spectral resolution. In the current work, we propose a simple algorithm which can suppress...
Using a set of low resolution images with sub-pixel shifts to reconstruct a high resolution less aliased image requires both interleaving of the image samples at the effectively higher sampling rate and deconvolution of the blur introduced by pixel sensor averaging. When measurement noise is low and knowledge of sub-pixel shift values is accurate, resolution improvement is limited primarily by the...
In this paper, we propose a new learning-based approach for super resolution image reconstruction utilizing total variation regularization method. By using the total variation (TV) regularization decomposition, we obtain the structure component which consists of edge component and the texture component which does not include edge component of the image. We use the texture component for the learning-based...
Near-infrared spectorscopy (NIRS) is a noninvasive neuroimaging technique that recently has been developed to measure the changes of cerebral blood oxygenation associated with brain activities. Conventional NIRS data analysis methods for brain mapping application normally consist of two stages, a preprocessing stage for noise removal followed by a mapping stage. This paper describes a wavelet packet...
There are many applications of radar imaging and radar vision. Such systems can be used for intrusion detection, concealed weapons detection, monitoring of various constructions etc. 3D radar images can give detailed and precise information about spatial distribution of scatterers. Such images can be generated using SAR principle. Conventional SAR uses one dimension synthetic aperture which gives...
Images of high-resolution are desired and often required in most photoelectronic imaging applications, and corresponding image restoration algorithm has became the frontier research. A novel multiframe super-resolution reconstruction algorithm based on stochastic regularization is proposed in this paper. By analyzing the image degradation model, the iterative gradient method based on Taylor series...
A new algorithm for the reduction of blocking artifacts in images compressed using block-based discrete cosine transform (DCT) is proposed in this paper. Firstly, a Bayesian model and the Markov network assumption are adopted for our deblocking algorithm. An input blocking image is divided into observation nodes of the network. Then a simplified method is applied to find approximate optimal solutions...
The tau-p transform is one of the important techniques to eliminate coherent noises and separate wave fields. Operator aliasing artifacts and edge effect artifacts affect its resolution. Therefore, conventional tau-p transform can't be used simply. This paper presents a new non-iteration high-resolution tau-p transform procedure, which can suppress aliasing artifacts and edge effect artifacts simultaneously,...
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