Showing papers on "Image scaling published in 2003"

Adaptive homogeneity-directed demosaicing algorithm

Abstract: Most cost-effective digital camera uses a single image sensor, applying alternating patterns of red, green, and blue color filters to each pixel location. Demosaicing algorithm reconstructs a full three-color representation of color images from this sensor data. This paper identifies three inherent problems often associated with directional interpolation approach to demosaicing algorithms: misguidance color artifacts, interpolation color artifacts, and aliasing. The level of misguidance color artifacts present in two images can be compared using metric neighborhood modeling. The proposed demosaicing algorithm estimates missing pixels by interpolating in the direction with fewer color artifacts. The aliasing problem is addressed by applying filterbank techniques to directional interpolation. The interpolation artifacts are reduced using a nonlinear iterative procedure. Experimental results using digital images confirm the effectiveness of this approach.

Winscale: an image-scaling algorithm using an area pixel model

Abstract: We propose a new scaling algorithm, winscale, which performs the scale up/down transform using an area pixel model rather than a point pixel model. The proposed algorithm has low complexity: the algorithm uses a maximum of four pixels of an original image to calculate one pixel of a scaled image. Nevertheless, the algorithm has good characteristics such as fine-edge and changeable smoothness. We implemented a hardware design of winscale using an FPGA and displayed some test scenes in an liquid crystal display panel using a digital visual interface. The hardware cost and the image quality were compared with those of the conventional image scaling algorithms. It is proved that winscale has good scale property with low complexity. Winscale can be used in various digital display devices that need image scaling, especially in applications that require good image quality with low hardware cost.

Subpixel image scaling for color‐matrix displays

Abstract: The perceived resolution of matrix displays increases when the relative position of the color subpixels is taken into account. Subpixel-rendering algorithms are being used to convert an input image to subpixel-corrected display images. This paper deals with the consequences of the subpixel structure and the theoretical background of the resolution gain. We will show that this theory allows a low-cost implementation in an image scaler. This leads to high flexibility, allowing different subpixel arrangements and a simple control over the trade-off between perceived resolution and color errors.

Method and system for selecting interpolation filter type in video coding

Abstract: A method and system for coding a video sequence based on motion compensated prediction (642), wherein an interpolation filter (640) is used to generate predicted pixel values for picture blocks in the video sequence. The interpolation filter for use in conjunction with a multi-picture type is shorter or having fewer coefficients than the interpolation filter for use in conjunction with a single-picture type. As such, the complexity of the interpolation filter for the multi-picture type can be reduced. Furthermore, the interpolation filter may be changed based on the characteristics of the block, the size and/or the shape of the block.

Image resampling and constraint formulation for multiframe superresolution restoration

Abstract: Multi-frame super-resolution restoration algorithms commonly utilize a linear observation model relating the recorded images to the unknown restored image estimates. Working within this framework, we demonstrate a method for generalizing the observation model to incorporate spatially varying point spread functions and general motion fields. The method utilizes results from image resampling theory which is shown to have equivalences with the multi-frame image observation model used in super-resolution restoration. An algorithm for computing the coefficients of the spatially varying observation filter is developed. Examples of the application of the proposed method are presented.

Two-dimensional cubic convolution

Abstract: The paper develops two-dimensional (2D), nonseparable, piecewise cubic convolution (PCC) for image interpolation. Traditionally, PCC has been implemented based on a one-dimensional (1D) derivation with a separable generalization to two dimensions. However, typical scenes and imaging systems are not separable, so the traditional approach is suboptimal. We develop a closed-form derivation for a two-parameter, 2D PCC kernel with support [-2,2]/spl times/[-2,2] that is constrained for continuity, smoothness, symmetry, and flat-field response. Our analyses, using several image models, including Markov random fields, demonstrate that the 2D PCC yields small improvements in interpolation fidelity over the traditional, separable approach. The constraints on the derivation can be relaxed to provide greater flexibility and performance.

Device, method and program for generating interpolation frame

Abstract: An interpolation frame generation device that generates an interpolation frame that interpolates image frames that are obtained by decoding a coded image signal that is coded by motion compensation, includes a motion vector deriving unit and an interpolation frame generating unit. The motion vector deriving unit acquires a motion compensation vector of a coded block that forms the coded image signal. The interpolation frame generating unit generates the interpolation frame in accordance with the motion vector of the image block that forms an image frame by using the motion compensation vector of the coded block as the motion vector of the image block.

Smart interpolation by anisotropic diffusion

Abstract: To enlarge a digital image from a single frame preserving the perceptive cues is a relevant research issue. The best known algorithms take into account the presence of edges in the luminance channel, to interpolate correctly the samples/pixels of the original image. This approach allows the production of pictures where the interpolated artifacts (aliasing blurring effect,...) are limited but where high frequencies are not properly preserved. The zooming algorithm proposed in this paper on the other hand reduces the noise and enhances the contrast to the borders/edges of the enlarged picture using classical anisotropic diffusion improved by a smart heuristic strategy. The method requires limited computational resources and it works on gray-level images, RGB color pictures and Bayer data. Our experiments show that this algorithm outperforms in quality and efficiency the classical interpolation methods (replication, bilinear, bicubic).

Progressive scan method used in display using adaptive edge dependent interpolation

Abstract: Provided is a progressive scan method used in a display using adaptive edge interpolation. According to the progressive scan method, a final edge direction that satisfies a first edge-determination condition and a second edge-determination condition is detected by performing interpolation for 7×3 pixel windows, using code determination and a comparison of a standard deviation based on differences between luminances of pixel data divided by an edge boundary. As a result, directional edge interpolation is carried out in a region of a low gradient below 45° and to 27° at the minimum, and simple intra-field linear interpolation can be performed in a high-frequency texture region. Subsequently, it is possible to remove high-frequency noise introduced in edge dependent interpolation or unnatural screen display due to zigzagged edges, thereby improving the quality of a display.

Edge direction based image interpolation method

Abstract: A method for interpolating pixel data of an omitted line by use of pixel data from an interlaced scan, for de-interlacing an interlaced video image. Image edge direction is detected at the center position of every two neighboring scan lines in an interlaced scan. All the directions detected in a given field constitute an edge orientation map. Edge directions are filtered to remove false and unreliable edge directions from the edge orientation map. If an edge direction is removed, the vertical edge direction is used to replace that direction in the edge orientation map. For interpolating a new pixel at the center of two neighboring scan lines, the corresponding direction for that position is used as the interpolation direction to calculate the value of the new pixel. If the direction is vertical, a filter is used along the vertical direction to calculate the interpolation value. If the direction is non-vertical, and has an integer value, then interpolation is performed by taking the average of the two neighboring sample values along the direction. If the direction is non-vertical and has a non-integer value, then an interpolation value is calculated using a directional bilinear method.

Further analysis of interpolation effects in mutual information-based image registration

Abstract: This paper presents an analysis of the mutual information (MI) metric in rigid-body registration of two digital images, in particular, local fluctuations of the MI value due to interpolation. In contrast to existing work in this area, this paper starts with two hypothetical continuous images, based on which both sampling and interpolation effects are analyzed. This analysis indicates that an "ideal" interpolator may not be able to completely suppress the undesirable local minima of the MI metric if the sampling effect is not negligible. Several preprocessing methods are discussed for reducing the interpolation effects.

Boundary effect free and adaptive discrete signal sinc-interpolation algorithms for signal and image resampling.

Abstract: The problem of digital signal and image resampling with discrete sinc interpolation is addressed. Discrete sinc interpolation is theoretically the best one among the digital convolution-based signal resampling methods because it does not distort the signal as defined by its samples and is completely reversible. However, sinc interpolation is frequently not considered in applications because it suffers from boundary effects, tends to produce signal oscillations at the image edges, and has relatively high computational complexity when irregular signal resampling is required. A solution that enables the elimination of these limitations of the discrete sinc interpolation is suggested. Two flexible and computationally efficient algorithms for boundary effects free and adaptive discrete sinc interpolation are presented: frame-wise (global) sinc interpolation in the discrete cosine transform (DCT) domain and local adaptive sinc interpolation in the DCT domain of a sliding window. The latter offers options not available with other interpolation methods: interpolation with simultaneous signal restoration/enhancement and adaptive interpolation with super resolution.

Image-based rendering by joint view triangulation

Abstract: The creation of novel views using prestored images or image-based rendering has many potential applications, such as visual simulation, virtual reality, and telepresence, for which traditional computer graphics based on geometric modeling would be unsatisfactory particularly with very complex three-dimensional scenes. This paper presents a new image-based rendering system that tackles the two most difficult problems of image-based modeling: pixel matching and visibility handling. We first introduce the joint view triangulation (JVT), a novel representation for pairs of images that handles the visibility and occlusion problems created by the parallaxes between the images. The JVT is built from matched planar patches regularized by local smooth constraints encoded by plane homographies. Then, we introduce an incremental edge-constrained construction algorithm. Finally, we present a pseudo-painter's rendering algorithm for the JVT and demonstrate the performance of these methods experimentally.

Method and apparatus for image interpolation based on adaptive polyphase filters

Abstract: An interpolation system interpolates image positions in an original image to produce an interpolated output image, wherein the original image is represented by digital input pixel data. A first filter with a sharp interpolation characteristic, that interpolates a selected image position in the image to generate a sharp interpolation output value. A second filter having a smooth interpolation characteristic, that interpolates the selected image position in the image to generate a smooth interpolation output value. A controller that calculates a weighting coefficient for the output of each filter. And, a combiner selectively combines the output values from the filters as a function of the weighting coefficients, to generate an interpolation output value for the selected image position of an interpolated output image.

EM-based simultaneous registration, restoration, and interpolation of super-resolved images

Abstract: A maximum likelihood (ML) solution to the problem of obtaining high-resolution images from sequences of noisy, blurred, and low-resolution images is presented. In our formulation, the registration parameters of the low-resolution images, the degrading blur, and noise variance are unknown. Our algorithm has the advantage that all unknown parameters are obtained simultaneously using all of the available data. An efficient implementation is presented in the frequency domain, based on the expectation maximization (EM) algorithm. Simulations demonstrate the effectiveness of the algorithm.

A motion-adaptive de-interlacing method using an efficient spatial and temporal interpolation

Abstract: This paper proposes a motion-adaptive de-interlacing algorithm based on an edge-based median filter (EMF) and adaptive minimum pixel difference filter (AMPDF). To compensate for missing-motion error, which is an important factor in motion-adaptive methods, we used an AMPDF, which estimates an accurate value using different thresholds after classifying areas of the input image into 3 classes. To efficiently interpolate moving diagonal edges, we used an EMF, which adopted a 5-point median filter using the edge information. Finally, to increase the performance, we adopted adaptive interpolation after subdividing the input image into moving, background, and boundary regions. Simulation results showed that the proposed method performs better than existing methods.

Neural Network Image Scaling Using Spatial Errors

Abstract: —We propose a general method for gradient-basedtraining of neural network (NN) models to scale multi-dimensional signal data. In the case of image data, the goal is to fitmodels that produce images of high perceptual quality, as opposedto simply a high peak signal to noise ratio (PSNR). There havebeen a number of perceptual image error measures proposed inthe literature, the majority of which consider the behavior ofthe error surface in some local neighborhood of each pixel. Byintegrating such error measures into the NN learning framework,we may fit models that minimize the perceptual error, producingresults that are more visually pleasing. We introduce a spatialerror measure and discuss in detail the derivative computationsnecessary for backpropagation. The results are compared toneural networks trained with the standard sum of squared errors(SSE) function, as well as a state of the art scaling method.Index Terms—image error measures, image scaling, imageinterpolation, super-resolution, neural networks.

Multi-resolution image inpainting

Abstract: Digital inpainting is an image interpolation mechanism, which can automatically restore damaged or partially removed image. Most inpainting mechanisms use a singular resolution approach on the extrapolation or interpolation of pixels. We propose a multi-resolution algorithm, which can take into consideration the different levels of details. The algorithm was tested on 1000 still images, with an evaluation showing the effectiveness of our approach. The demonstration of our work is available at: http://www.mine.tku.edu.tw/demos/inpaint.

Superresolution remote sensing image processing algorithm based on wavelet transform and interpolation

Abstract: According to characteristic of image wavelet transform and interpolation, this paper proposes a remote sensing image interpolation method combining wavelet transform and interpolation algorithm, which can improve the remote sensing image resolution. Experiments show that the algorithm can properly retain abundant high frequency information in original remote sensing image. After interpolation processing and wavelet reconstruction, we can obtain a remote sensing image with higher resolution, better visual effect, higher Signal Noise Ratio (SNR), more detail information and no apparent warp. Therefore, this algorithm is an effective method of super-resolution remote sensing image processing.

Multi-image registration for an enhanced vision system

Abstract: An Enhanced Vision System (EVS) utilizing multi-sensor image fusion is currently under development at the NASA Langley Research Center. The EVS will provide enhanced images of the flight environment to assist pilots in poor visibility conditions. Multi-spectral images obtained from a short wave infrared (SWIR), a long wave infrared (LWIR), and a color visible band CCD camera, are enhanced and fused using the Retinex algorithm. The images from the different sensors do not have a uniform data structure: the three sensors not only operate at different wavelengths, but they also have different spatial resolutions, optical fields of view (FOV), and bore-sighting inaccuracies. Thus, in order to perform image fusion, the images must first be co-registered. Image registration is the task of aligning images taken at different times, from different sensors, or from different viewpoints, so that all corresponding points in the images match. In this paper, we present two methods for registering multiple multi-spectral images. The first method performs registration using sensor specifications to match the FOVs and resolutions directly through image resampling. In the second method, registration is obtained through geometric correction based on a spatial transformation defined by user selected control points and regression analysis.

Image interpolation system and image interpolation method

Abstract: An image interpolation system for interpolating the gaps between the lines forming an image, includes: virtual interpolation data generating means 100 and 120 for generating virtual interpolation data of inter-lines between the lines of the input image, based on the input image line data; and interpolating means 130 and 140 for interpolating the pixels between input image lines, based on the generated virtual interpolation data.

Fast high precision matching method

Abstract: An initial search method uses the input image and the template to create an initial search result output. A high precision match uses the initial search result, the input image, and the template to create a high precision match result output. The high precision match method estimates high precision parameters by image interpolation and interpolation parameter optimization. The method also performs robust matching by limiting pixel contribution or pixel weighting. An invariant high precision match method estimates subpixel position and subsampling scale and rotation parameters by image interpolation and interpolation parameter optimization on the log-converted radial-angular transformation domain.

Method of and apparatus for extending signal ranges of digital images

Abstract: The invention is a method and apparatus to extend the signal range of a digital image beyond the nominal sensor or data format range. The method and apparatus automatically acquires a scaled series of source data, applies noise reduction to the source data, and constructs a scaled composite with usable signal ranges greater than that of the individual data sources. Applied to digital images, the invention permits presentation and analysis of all signals from a subject in a single composite or an image resulting from the method and apparatus of the present invention. The present invention overcomes two defects in prior art systems: increased noise in the resultant composite image arising from rescaling of component images and dependence on evaluating image content to determine image scaling. Because this invention can be automated, it can be applied in numerous fields requiring high throughput.

Optimal framework for low bit-rate block coders

Abstract: Block coders are among the most common compression tools available for still images and video sequences. Their low computational complexity along with their good performance make them a popular choice for compression of natural images. Yet, at low bit-rates, block coders introduce visually annoying artifacts into the image. One approach that alleviates this problem is to downsample the image, apply the coding algorithm, and interpolate back to the original resolution. In this paper, we consider the use of optimal decimation and interpolation filters in this scheme. We first consider only optimization of the interpolation filter, by formulating the problem as least-squares minimization. We then consider the joint optimization over both the decimation and the interpolation filters, using the variable projection method. The experimental results presented clearly exhibit a significant improvement over other approaches.

Fast edge-oriented image interpolation algorithm

Abstract: The invention provides an image interpolating method, which comprises receiving the low resolution pixels Y ij . Then, a homogenous area and an edge area of the image are determined according to pixel differences of the pixels Y 2i, 2j in comparing with a threshold. Then, the pixels Y 2i,2j belonging to the homogenous area are interpolated by a first interpolating algorithm, while the pixels Y 2i,2j belonging to the edge area are interpolated by a second interpolating algorithm.

An efficient directional image interpolation method

Abstract: The paper introduces a new method for interpolation in which the angular orientation of image features is exploited to enhance subjective quality. This involves, as a first step, extracting the low frequency information and expanding its size in a parallel channel. The mid to high frequency information is decomposed directionally into angular subbands. The subbands are then interpolated to enhance edge definition and then recombined. Experimental results show that the subjective quality of the interpolation is improved relative to conventional methods. Moreover, the interpolation method, when combined with JPEG compression, enables significantly higher coding performance to be achieved at low bit-rates - bit-rates at which JPEG typically breaks down.

Method and device for image interpolation with motion compensation

Abstract: A method of interpolating images intended to be incorporated, into a sequence of moving images, each between a first original image and a second original image of the sequence, comprises an estimation of a motion vector associated with a given pixel block of a current interpolated image. This estimation comprises the preselection of P first motion vectors associated with first other pixel blocks that are adjacent to the given pixel block in the current interpolated image, for which there is already an estimated motion vector. It also comprises the preselection of at most Q second motion vectors associated respectively with second other pixel blocks adjacent to the given pixel block in the preceding interpolated image. Finally, it comprises the selection of the motion vector which minimizes a cost function from the first and second preselected motion vectors.

A grayscale image interpolation method using new morphological skeleton

Abstract: This paper addresses the gray scale image interpolation by means of mathematical morphology. The new image interpolation method, called skeleton interpolation is based on morphological 3D binary skeleton. This article will present the theoretical background of the morphological image interpolation, deduce the new representation and show some application examples. Computer simulations illustrate results.