This paper proposes a scale correlation-based edge detection scheme. A scale correlation function is defined as the product of detection filter's response at two scales. With the proper choice of detection filters such as the first derivative of Gaussian, the scale correlation will magnify the edge structures and suppress the noise. Unlike many of the multiscale techniques that first form the edge maps at several scales and then synthesize them together, in our scheme, edges are determined as the local maxima directly in the correlation function. The detection and localization criteria of the scale correlation are defined. It is shown that with little loss in detection criterion, much improvement is gained on localization criterion. Using scale correlation, the dislocation of neighboring edges is also improved when the width of detection filter is set large to smooth noise.
It is of great importance in image restoration to remove noise while preserving and enhancing edges. This paper presents a spatial correlation thresholding scheme for image restoration. The dyadic wavelet transform that acts as a Canny edge detector is employed here to characterize the significant structures, which would be strongly correlated along the wavelet scales. A correlation function is defined as the multiplication of two adjacent wavelet subbands with a translation to maximize the mathematical expectation. In the correlation function, edge structures are more discriminable because they are amplified while noise being diluted. Unlike most of the traditional schemes that threshold directly the wavelet coefficients, the proposed scheme applies thresholding on the correlation function to better preserve edges while suppressing noise. A robust threshold is presented and the experiment shows that the proposed scheme outperforms the traditional thresholding schemes not only in SNR comparison but also in the edge preservation.
Scene appearance for a continuous range of viewpoint can be represented by a discrete set of images via image morphing. In this paper, we present a new robust image morphing scheme based on 2D wavelet transform and interval field interpolation. Traditional mesh-base and field-based morphing algorithms, usually designed in the spatial image space, suffer from very high time complexity and therefore make themselves impractical in real-time virtual environment applications. Compared with traditional morphing methods, the proposed wavelet-based interval morphing scheme performs interval interpolation in both the frequency and spatial spaces. First, the images of the scene can be significantly compressed in the frequency domain with little degradation in visual quality and therefore the complexity of the scene can be significantly reduced. Second, since a feature point in the image may correspond to a neighborhood in a subband image in the wavelet domain, we define feature interval for the wavelet-transformed images for an accurate feature matching between the morphing images. Based on the feature intervals, we employ the interval field interpolation to morph the images progressively in a coarse-to-fine process. Finally, we use a post-warping procedure to transform the interpolated views to its desired position. A nice future of using wavelet transformation is its multiresolution representation mode, which enables the progressive morphing of scene.
This paper presents a new view synthesis techniques using morphing and 2-d discrete wavelet transformation. We completely base on pairwise images that are known without calibrating camera and the depth information of images. First, we estimate the Fundamental Matrix related with any pair of images. Second, using fundamental matrix, any pair of image planes can be rectified to be parallel and their corresponding points are lying on the same scanline. This gives an opportunity to generate new views with linear interpolating technique. Third, the pre-warped images are then decomposed into hierarchical structure with wavelet transformation. Corresponding coefficients between two decomposed images are therefore linear interpolated to form the multiresolution representation of an intermediate view. Any quantization techniques can be embedded here to compress the coefficients in depth. The compressed format is very suitable for storage and communication. Fourthly, when displaying, compressed images are decoded and an inverse wavelet transform is achieved. Finally, we use a post-warping procedure to transform the interpolated views to its desired position. A nice future of using wavelet transformation is its multiresolution representation mode, which makes generating views can be refined progressively and hence suitable for communication.
In this paper, we introduce a new approach for edge preserving image compression technique based on the wavelet transform and iterative constrained least square regularization approach. This approach treats image reconstructed from lossy image compression as the process of image restoration. It utilizes the edge information detected from the source image as a priori knowledge for the subsequent reconstruction In order to compromise the overall bit rate incurred by the additional edge information, a simple vector quantization scheme is proposed to classify the edge bit-planes pattern into a number of binary codevectors. The experiment showed that the proposed approach could definitely prove both objective and subjective quality of the reconstructed image by recovering more image details and edges.
Image Registration, one of the scene encoding approaches, is a very active research topic in computer vision and computer graphics community. This paper presents a robust image registration scheme that employs complex wavelet pyramid and the human visual perceptive thresholding techniques. Complex wavelet transform guarantees not only a global optimal solution, but also the scale and translation invariance for the image alignment. Applying the Human Visual System thresholding for wavelet coefficients shows that image can be compressed significantly (30-100:1 ratio) while the detail of the structured information can be retained so that the transformation obtained from the thresholded wavelet images is sufficiently accurate when applying on the original images. The transformation can be progressively refined on the multiresolution decomposition. This guarantees the robustness of the scheme with a better performance than the traditional registration techniques. Moreover, the scheme registers images taken directly by hand-held digital camera without knowing camera motion and any intrinsic parameters of camera.
The near-lossless CALIC is one of the best near-lossless intraframe image coding schemes which exploits and removes the local context correlation of images. Wavelet transform localizes the frequency domain and exploits the frequency- based global correlation of images. Applying the context modeling for the wavelet transform coefficients, a state of the art intraframe near-lossless coding scheme can be obtained. In this paper, we generalize the intraframe wavelet transform CALIC to interframe coding to form a hybrid near-lossless multispectral image compression. Context modeling techniques lend themselves easily to modeling of image sequences. While wavelet transform exploits the global redundancies, the interframe context modeling can thoroughly exploit the statistical redundance is both between and within the frames. First, the image frame is wavelet transformed in the near-lossless mode to obtain a set of orthogonal subclasses of images. Then the coefficients of interframes of the image are predicted using the gradient-adjusted predictor based on both intra- and inter-frame current coefficient context. The predicted coefficients are adjusted predictor based on both intra- and inter-frame current coefficient context. The predicted coefficients are adjusted using the sample mean of prediction errors conditioned on the current context and the residues are quantized. An incremental scheme is used for the prediction errors in a moving time windows for prediction bias cancellation. All the components are distortion controlled in the minmax metric to ensure the near-lossless compression. The decompression is the inverse of the process. It is demonstrated that the near-lossless wavelet transform and context modeling interframe image compression is one of the best schemes in high-fidelity multispectral image compression and it outperforms its intraframe counterpart with 10-20 percent compression gains while keeping the high fidelity.
In this paper, we propose to incorporate both spatial and frequency models of HVS into wavelet transform image coding. The process of wavelet transform decomposition, which splits the spatial frequency domain to several octave bands by dilation and translation of a single basic wavelet, is similar to that of frequency model HVS. Moreover, according to spatial model of HVS, some compact physical features like contours and regions are with high visual perceptive significance to human vision system. Based on the spatial model we develop a visual perception sensitive map and use the map to develop a wavelet thresholding scheme in order to achieve a high image compression ratio while retaining a high visual quality of the reconstructed image.After removing the less visually significant coefficients, we developed an adaptive quantization scheme for transformed coefficients at each of the subbands. This quantization scheme is developed based on the HVS frequency model to minimize the visual error due to quantization. In our image compression system, both frequency and spatial aspects of HVS to the image have been taken into consideration. We preserve the highly visual perceptive wavelet coefficients and minimize the visual distortion of coefficients in each of the decomposed band. As a result, a high compression ratio and low visual distortion coder is obtained.
In this paper, we propose to incorporate both spatial and frequency models of HVS into wavelet transform image coding. The process of wavelet transform decomposition, which splits the spatial frequency domain to several octave bands by dilation and translation of a single basic wavelet, is similar to that of frequency model HVS. Moreover, according to spatial model of HVS, some compact physical features like contours and regions are with highly visually significant to human vision system. Based on the spatial model, we apply fuzzy logic theory to detect visual significant edge points and based on these edge points to construct a Visual Perception Sensitive Map (VPSM) for wavelet coefficient thresholding scheme. Only the visual significant coefficients are retained and the rest are discard. This approach can achieve a high image compression ratio while minimizing the visual quality distortion of the reconstructed image. In addition, we develop an adaptive quantization scheme for the wavelet coefficients at each of the subbands. This quantization scheme is developed based on the HVS frequency model to minimize the visual errors caused by the quantization. In our image compression system, both the frequency and spatial aspects of HVS to the image have been taken into consideration. We preserve the highly visual perceptive wavelet coefficients and minimize the visual distortion of coefficients in each of the decomposed band. As a result, a high compression ratio with low visual distortion coder is obtained.
Digital cameras are gaining popularity in many applications of multimedia information processing. But the CCD sensor used by digital cameras does not provide all three red, green, blue primaries for each pixel. Instead it uses an interlaced sampling scheme with only one primary per pixel. This article considers the problem of reconstructing a 24- bit/pixel color image from the interlaced sampling. A simple, efficient, and effective algorithm for color restoration from digital camera data is proposed. The proposed algorithm uses a pattern matching technique to reconstruct the missing color primaries based on the pixel contexts. Experimental results show that the proposed algorithm outperforms the technique of color interpolation.