Fast computation of residual complexity image similarity metric using low‐complexity transforms

Automatic recognition of scenes with power line wires in real life aerial images using DCT-based features

For numerous applications in image registration, sub-pixel translation estimation is a fundamental task, and increasing attention has been given to methods based on image phase information. However, we have found that none of these methods is universal. In other words, for any one of these methods, we can always find some image pairs which will not be well matched. In this paper, by introducing the cyclic shift matrix (CSM), we present a new model for the translation matching problem and derive a least squares solution for the model. In addition, by repeatedly applying the CSM to the matching image, an iterative CSM method is proposed to further improve the matching accuracy. Furthermore, we show that the traditional phase-based matching algorithms can only achieve an exact solution when there is a cyclic shift relationship between the images to be matched. The proposed method is evaluated using simulated and real images and demonstrates a better performance in both accuracy and robustness compared with the state-of-the-art methods.

An image registration technique for compressed video such as motion JPEG or the I picture of MPEG based on the matching of DCT (Discrete Cosine Transform) coefficients is investigated in this research. Several simple features such as the DC value and a couple of low-frequency AC coefficients in the DCT domain are first extracted to indicate the edge strength and orientation inside each block for the image alignment purpose. Next, we conduct a coarse-level image segmentation task to filter out irrelevant regions. Then, for the regions of interest, we perform a more detail analysis to get the edge map. Finally, the alignment parameters are determined based on the information contained by the edge map. It is shown by experimental results that the proposed method reduces the computational cost of image registration dramatically as compared with the pixel domain registration technique while achieving certain quality of composition.

The Discrete Cosine Transform (DCT) is largely used for image and video compression in standards like JPEG and MPEG2. Its simplicity coupled with the fact that it can be computed faster than the Discrete Wavelet Transform (DWT) makes it an attractive option for image and video compression. Consequently a lot of research is in progress to determine novel architectures and algorithms for faster DCT computation having high throughput so that it can even be used for real time applications. For example, when transmitting and receiving videos, which have been compressed using MPEG2, one would like to receive the video, without any buffering delay. In other words, one requires such an architecture, which provides the DCTQ coeffecients at a consistent rate, with very low latency between two coeffecients. As such, a highly parallel and pipelined architecture employing 57 pipeline stages and using fast multipliers and adders is proposed in this work. The proposed DCT Processor is implemented in Spartan 3E FPGA.

Imaging of the secondary electron bremsstrahlung (SEB) x rays emitted during particle-ion irradiation is a promising method for beam range estimation. However, the SEB x-ray images are not directly correlated to the dose images. In addition, limited spatial resolution of the x-ray camera and low-count situation may impede correctly estimating the beam range and width in SEB x-ray images. To overcome these limitations of the SEB x-ray images measured by the x-ray camera, a deep learning (DL) approach was proposed in this work to predict the dose images for estimating the range and width of the carbon ion beam on the measured SEB x-ray images. To prepare enough data for the DL training efficiently, 10,000 simulated SEB x-ray and dose image pairs were generated by our in-house developed model function for different carbon ion beam energies and doses. The proposed DL neural network consists of two U-nets for SEB x ray to dose image conversion and super resolution. After the network being trained with these simulated x-ray and dose image pairs, the dose images were predicted from simulated and measured SEB x-ray testing images for performance evaluation. For the 500 simulated testing images, the average mean squared error (MSE) was 2.5×10-5 and average structural similarity index (SSIM) was 0.997 while the error of both beam range and width was within 1mm FWHM. For the three measured SEB x-ray images, the MSE was no worse than 5.5×10-3 and SSIM was no worse than 0.980 while the error of the beam range and width was 2mm and 5mm FWHM, respectively. We have demonstrated the advantages of predicting dose images from not only simulated data but also measured data using our deep learning approach.

Background: Non-rigid registration of medical images with intensity distortions is a difficult problem due to the change in pixel intensity. It is caused by contrast agent or intensity bias field. Methods: In some cases, this problem can be solved using Residual Complexity (RC) method. However, relative modification of parameter in residual complexity would result in completely different experimental effect. Another drawback is sensitivity to noise. To handle this problem, a new intensity-based similarity measure, Weighted Residual Complexity (WRC) has been proposed for effective medical image registration in this paper. Specifically, the local entropy image of two images is computed to be aligned respectively. Then, a weighting function using a function of the local entropy difference is modeled. The weighting function is used to weight the residual image in residual complexity adaptively. The residual image is defined as the difference between reference image and warped floating image. Results: The weighting function assigns smaller weight to residual image if the corresponding pixel value is larger in local entropy difference. The proposed technique was applied to simulative and real medical images. The contrast experiments were made with mutual information, diffeomorphic demons and residual complexity. Conclusion: Also, the analysis of experimental results was made qualitatively and quantitatively, which indicates that this new approach gives a better performance than diffeomorphic demons, mutual information and residual complexity. Keywords: Weighting function, local entropy, residual complexity, intensity distortion, image registration, WRC.

Distance transforms (DTs) are standard tools in image analysis, with applications in image registration and segmentation. The DT is based on extremal (minimal) distance values and is therefore highly sensitive to noise. We present a stochastic distance transform (SDT) based on discrete random sets, in which a model of element-wise probability is utilized and the SDT is computed as the first moment of the distance distribution to the random set. We present two methods for computing the SDT and analyze them w.r.t. accuracy and complexity. Further, we propose a method, utilizing kernel density estimation, for estimating probability functions and associated random sets to use with the SDT. We evaluate the accuracy of the SDT and the proposed framework on images of thin line structures and disks corrupted by salt and pepper noise and observe excellent performance. We also insert the SDT into a segmentation framework and apply it to overlapping objects, where it provides substantially improved performance over previous methods. Finally, we evaluate the SDT and observe very good performance, on simulated images from localization microscopy, a state-of-the-art super-resolution microscopy technique which yields highly spatially localized but noisy point-clouds.

In this paper a method is presented for copyright protection in digital images. Copyright protection is achieved by embedding an invisible signal, known as digital signature or watermark, in the digital image. The method proposed in this paper casts the signature in the frequency domain by slightly modifying the values of randomly selected DC coefficients of the Discrete Cosine Transform (DCT) of the image. The same method is applied also on the Subband or Wavelet Transform coefficients. An adaptive method is proposed also based on perceptual criteria that guarantees the invisibility of the watermark and avoids the deterioration of the image. Signature detection is done via hypothesis testing, without to use any information from the original image. The watermarks embedded by the proposed method are very resistant to JPEG and other frequently used compression. Experimental results using real image data verify the effectiveness of the method.

Image up-sampling in the discrete cosine transform (DCT) domain is a challenging problem because DCT coefficients are de-correlated, such that it is nontrivial to estimate directly high-frequency DCT coefficients from observed low-frequency DCT coefficients. In the literature, DCT-based up-sampling algorithms usually pad zeros as high-frequency DCT coefficients or estimate such coefficients with limited success mainly due to the nonadaptive estimator and restricted information from a single observed image. In this paper, we tackle the problem of estimating high-frequency DCT coefficients in the spatial domain by proposing a learning-based scheme using an adaptive k-nearest neighbor weighted minimum mean squares error (MMSE) estimation framework. Our proposed scheme makes use of the information from precomputed dictionaries to formulate an adaptive linear MMSE estimator for each DCT block. The scheme is able to estimate high-frequency DCT coefficients with very successful results. Experimental results show that the proposed up-sampling scheme produces the minimal ringing and blocking effects, and significantly better results compared with the state-of-the-art algorithms in terms of peak signal-to-noise ratio (more than 1 dB), structural similarity, and subjective quality measurements.

One problem on binocular disparity estimation is, given a point in one view of a scene, to find the homologous point in another view from an adjacent camera. In this work, we use the discrete cosine transform (DCT) with evolutionary methods as an approach to obtain the disparity map. The disparity estimation process is implemented by generating for each image block sets of DCT coefficients. The job of optimising the DCT coefficients, whose inverse transform gives the disparity map, is carried out by a optimisation technique inspired by natural evolution: the genetic algorithm. Matching is performed in the image domain using an intensity similarity measure.

Image compression is a basic need for the progressive transmission or storage of image data via any media. For the effective processing of image data the coding standards were developed. The most commonly used standard in current scenario is the JPEG and JPEG-2000 coding. These standards have become the industry standards and are progressively been in use for almost all image processing applications. In the process of image coding various sub-processing operations occur which process the image blocks by transformation, quantization and encoding before transmitting or storing. In the process, quantization is observed to be a time consuming and is needed to be processed in high precision so as to achieve less processing error and also in reasonable time. The process of quantization has shown greater efficiency in vector quantization than earlier methods. Vector quantization is observed to be an efficient block based lossy compression technique. Due to simpler coding process and high compression ratio VQ are widely used. Predictive vector Quantization (PVQ) is commonly used memory based vector Quantization method. The main drawback with PVQ is the high computational complexity which limits PVQ to use in real time applications. Shorter coding time can be archived by reducing the number of code words or instructions, but the reconstructed image quality will degraded in the case of fewer code words. Hence, it is necessary to propose fast coding algorithms for PVQ. In this paper a modified version of the PVQ approach has been suggested in order to process faster, which ensue to be a compatible mechanism for real time image coding in JPEG based coding system. Keywords: Compression, quantization, PVQ encoder, mean-square-error, Discrete Cosine Transform.

The registration of intraoperative x-ray images with preoperative CT images is an important step in image-guided surgery. However, existing regression-based methods lack an interpretable and stable mechanism when fusing information from intraoperative images and preoperative CT volumes. In addition, existing feature extraction and fusion methods limit the accuracy of poseregression. The objective of this study is to develop a method that leverages both x-ray and computed tomography (CT) images to rapidly and robustly estimate an accurate initial registration within a broad search space. This approach integrates the strengths of learning-based registration with those of traditional registration methodologies, enabling the acquisition of registration outcomes across a wide search space at an acceleratedpace. We introduce a regression-based registration framework to address the aforementioned issues. We constrain the feature fusion process by training the network to reconstruct the high-dimensional feature representation vector of the preoperative CT volume in the embedding space from the input single-view x-ray, thereby enhancing the interpretability of feature extraction. Also, in order to promote the effective fusion and better extraction of local texture features and global information, we propose a lightweight cross-attention mechanism named lite cross-attention(LCAT). Besides, to meet the intraoperative requirements, we employ the intensity-based registration method CMA-ES to refine the result of pose regression. Our approach is verified on both real and simulated x-ray data. Experimental results show that compared with the existing learning-based registration methods, the median rotation error of our method can reach 1.9 and the median translation error can reach 5.6mm in the case of a large search range. When evaluated on 52 real x-ray images, we have a median rotation error of 1.6 and a median translation error of 3.8mm due to the smaller search range. We also verify the role of the LCAT and embedding reconstruction modules in our registration framework. If they are not used, our registration performance will be reduced to approximately random initialization results. During the experiments, our method demonstrates higher accuracy and larger capture range on both simulated images and real x-ray images compared to existing methods. The inspiring experimental results indicate the potential for future clinical application of ourmethod.

Image enlargement technique is widely used in image processing and video format transforming. This paper proposes an algorithm for fractional multiple image enlargement based on All Phase Discrete Cosine Transform (APDCT) interpolation. The All Phase Digital Filter (APDF) is a new type of linear phase filter. For a data vector with a length of N obtained by blocking a signal, there are N different phase data blocks that include the same sampling point. Through taking the mean of the N values as the filtering output, the APDF can eliminate different meanings of the orthogonal transform filtering values of those data vectors and thereby the block effect. According to this idea, this paper deduces the formula of two-dimension APDCT filter. As a good integration of Discrete Cosine Transform (DCT) and all phase idea, a family of separable interpolation kernel, which is constructed by cosine function, is put forward. Look-up method is then discussed for real time image enlargement. The objective and subjective experiments show that better image quality is obtained by using interpolation kernel proposed than using 6*6 cubic interpolation kernel.

In the last few decades, image registration (IR) has been established as a very active research area in computer vision. Over the years, IR's applications cover a broad range of real-world problems including remote sensing, medical imaging, artificial vision, and computer-aided design. In particular, medical IR is a mature research field with theoretical support and two decades of practical experience. Traditionally, medical IR has been tackled by iterative approaches considering numerical optimization methods which are likely to get stuck in local optima. Recently, a large number of medical IR methods based on the use of metaheuristics such as evolutionary algorithms have been proposed providing outstanding results. The success of the latter modern search methods is related to their ability to perform an effective and efficient global search in complex solution spaces like those tackled in the IR discipline. In this contribution, we aim to develop an experimental survey of the most recognized feature-based medical IR methods considering evolutionary algorithms and other metaheuristics. To do so, the generic IR framework is first presented by providing a deep description of the involved components. Then, a large number of the latter proposals are reviewed. Finally, the most representative methods are benchmarked on two real-world medical scenarios considering two data sets of three-dimensional images with different modalities.

Similarity measure is an important key in image registration. Most traditional intensity-based similarity measures (e.g. sum-of-squared-differences, correlation coefficient, mutual information and correlation ratio) assume a stationary image and pixel-by-pixel independence. These similarity measures ignore the correlation among pixel intensities; hence, a perfect image registration cannot be achieved especially in the presence of spatially varying intensity distortions and outlier objects that appear in one image but not in the other. It is supposed here that non-stationary intensity distortion (such as bias field) has a sparse representation in the transformation domain. Based on this assumption, a novel similarity measure is proposed here based on sparse representation in a mono-modal setting. The zero norm (l 0 ) in the transform domain is introduced as a new similarity measure in the presence of non-stationary intensity distortion. The present study defines a sparsity similarity measure that indicates the complexity of the residual image between two registered images in the transform domain such as discrete cosine transform or wavelet. It is attempted here to analytically and statistically illustrate that the proposed similarity measure has important properties such as metric properties in vector space, from correntropy perspective. This measure produces accurate registration results on both artificial and real-world problems which are examined in the present study.