Wavelets and image fusion

This paper describes the novel method related to correcting color distortion in color imaging apparatus. Color images acquired from CMOS or CCD digital sensors can suffer from color distortion, which means that images from the sensors are different from those human being sees in the color space. The one of main reasons of this color distortion is crosstalk between adjacent pixels. To correct this kind of color distortion, existing methods are generally to multiply some gain coefficients in each color channel and this multiplication can cause noise boost and loss of detail information. This paper proposes the novel method which can not only preserve color distortion correction accuracy, but also suppress noise boost and loss of detail information based on the image fusion. Specifically the color and low frequency information in luminance channel are extracted from the color transformed image. And high frequency information is from the original image before color transform. The method to extract the low and high frequency information is based on the multi-resolution analysis with edge preserving filters.

Gram-Schmidt and Ehlers fusion, which are well known for spectral fidelity, are described. Selecting a sandrock mine using SPOT5 as study area, spectral fidelity and high spatial information gain are used to assess the two fusion methods, which are compared with multiplication and Andorr fusion. In the whole, the Gram-Schmidt method is the best, preserving highly the original spectral information, and can provide spectrum control foundation for interpreting mine targets in the complex geology environment. Ehlers method is the second. Then Andorr method is the third, and it has the highest spatial information gain, but high frequency information is enlarged excessively, effecting on the identification of mine exploitation state. The multiplication method is the worst, because it loses the high and low frequency information, which is the most important for mine targets recognition.

Time–frequency domain based optimization of hedging strategy: Evidence from CSI 500 spot and futures

Multi-modality image fusion applied to improve image quality has drawn great attention from researchers in recent years. However, noise is actually generated in images captured by different types of imaging sensors, which can seriously affect the performance of multi-modality image fusion. As the fundamental method of noisy image fusion, source images are denoised first, and then the denoised images are fused. However, image denoising can decrease the sharpness of source images to affect the fusion performance. Additionally, denoising and fusion are processed in separate processing modes, which causes an increase in computation cost. To fuse noisy multi-modality image pairs accurately and efficiently, a multi-modality image simultaneous fusion and denoising method is proposed. In the proposed method, noisy source images are decomposed into cartoon and texture components. Cartoon-texture decomposition not only decomposes source images into detail and structure components for different image fusion schemes, but also isolates image noise from texture components. A Gaussian scale mixture (GSM) based sparse representation model is presented for the denoising and fusion of texture components. A spatial domain fusion rule is applied to cartoon components. The comparative experimental results confirm the proposed simultaneous image denoising and fusion method is superior to the state-of-the-art methods in terms of visual and quantitative evaluations.

Bilateral filtering has been applied in the area of digital image processing widely, but in the high gradient region of the image, bilateral filtering may generate staircase effect. Bilateral filtering can be regarded as one particular form of local mode filtering, according to above analysis, an mixed image de-noising algorithm is proposed based on Gaussian filter and bilateral filtering. First of all, it uses Gaussian filter to filtrate the noise image and get the reference image, then to take both the reference image and noise image as the input for range kernel function of bilateral filter. The reference image can provide the image’s low frequency information, and noise image can provide image’s high frequency information. Through the competitive experiment on both the method in this paper and traditional bilateral filtering, the experimental result showed that the mixed de-noising algorithm can effectively overcome staircase effect, and the filtrated image was more smooth, its textural features was also more close to the original image, and it can achieve higher PSNR value, but the amount of calculation of above two algorithms are basically the same.

In multichannel cochlear implants, low frequency information is delivered to apical cochlear locations while high frequency information is delivered to more basal locations, mimicking the normal acoustic tonotopic organization of the auditory nerves. In clinical practice, little attention has been paid to the distribution of acoustic input across the electrodes of an individual patient that might vary in terms of spacing and absolute tonotopic location. In normal-hearing listeners, Başkent and Shannon (J. Acoust. Soc. Am. 113, 2003) simulated implant signal processing conditions in which the frequency range assigned to the array was systematically made wider or narrower than the simulated stimulation range in the cochlea, resulting in frequency-place compression or expansion, respectively. In general, the best speech recognition was obtained when the input acoustic information was delivered to the matching tonotopic place in the cochlea with least frequency-place distortion. The present study measured phoneme and sentence recognition scores with similar frequency-place manipulations in six Med-El Combi 40+ implant subjects. Stimulation locations were estimated using the Greenwood mapping function based on the estimated electrode insertion depth. Results from frequency-place compression and expansion with implants were similar to simulation results, especially for postlingually deafened subjects, despite the uncertainty in the actual stimulation sites of the auditory nerves. The present study shows that frequency-place mapping is an important factor in implant performance and an individual implant patient's map could be optimized with functional tests using frequency-place manipulations.

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Image fusion is becoming one of the hottest techniques in image processing. Many image fusion methods have been developed in a number of applications. This paper mainly discusses the structures of image fusion process, which is classified as hierarchical fusion structure, overall fusion structure, and arbitrary fusion structure. And the effects of such image fusion structures on the performances of image fusion are analyzed. In the experiment, a typical hyperspectral image data set is fused using the same wavelet transform based image fusion technique, but applying different fusion structures. The differences among their fused images are analyzed. The experimental results testify the theoretical analysis that the performances of image fusion techniques are related not only to the fusion algorithm, but also to the fusion structures, and different image fusion structures that produces different fusion performance even using the same image fusion method.

Image fusion at pixel level without precise registration always causes pseudo colors and other problem. Classification-based fusion scheme can effectively eliminate the false color at the edge of objective. However, the traditional per-pixel classification results in the well-known salt and pepper effect. The only way to smooth the image is to use filters, while impacted on the result of fusion. This paper proposes a method consist of a sequential application of segmentation, classification and fusion techniques. First, the image was multi-resolutely segmented into homogenous areas, and classified it by using the membership functions classifier and additional empirical rules. Subsequently, according to the restriction of the precise classification result, adjusting the multi-spectral image then achieved the fusion by using HSV color transformation. Finally, after compared the statistical properties of the fusion result by different methods, the proposed method showed satisfied result. I. INTRODUCTION With the appearance of plentiful kinds of remote sensing imagery, fusion of satellite images of different spatial resolutions play an important role. Via image fusion, we can obtain more information than can be derived from single kind of image. Pohl prompted that according to the stage which image fusion is performed, there are pixel, feature and decision level. Image fusion at pixel level always was effected by the accuracy of registration, the number of mixed pixel and other factors. The pixel in high-resolution satellite image is smaller sizes and combined with fewer spectral bands that cause greater spectral variation within a class and a greater degree of shadow. Hence, color break and pseudo colors often occur at the edge of object and shadow which fusing the high-resolution image only at pixel levels. During the high-resolution image fusion, for eliminating color break and pseudo colors, using its classification image as the prior knowledge and restricting the fusion area can take full advantage of spectral information from multi-spectral bands and texture information come from pan band to realize the image fusion. It's a method of image fusion that performing at pixel level and integrate with feature level fusion. In this way, phenomenon of pseudo colors can be avoided. In this paper, we propose an image fusion method based on object-oriented classification. Via image segmentation, feature extraction and image fusion based on object-oriented classification, the fusion image can be achieved. In this method, object-oriented classification more appropriate for high-resolution image to get accuracy classification result and overcoming the limit of per-pixel image analyses. Because much information is acquired in the relationship between adjacent pixels, including texture and shape information, which allows for identification of individual objects as oppose to single pixels

With the increasing utilization of X-ray Computed Tomography (CT) in medical diagnosis, obtaining higher quality image with lower exposure to radiation is a highly challenging task in image processing. Sparse representation based image fusion is one of the sought after fusion techniques among the current researchers. A novel image fusion algorithm based on focused vector detection is proposed in this thesis. Firstly, the initial fused vector is acquired by combining common and innovative sparse components of multi-dosage ensemble using Joint Sparse PCA fusion method utilizing an overcomplete dictionary trained using high dose images of the same region of interest from different patients. And then, the strongly focused vector is obtained by determining the pixels of low dose and medium dose vectors which have high similarity with the pixels of the initial fused vector using certain quantitative metrics. Final fused image is obtained by denoising and simultaneously integrating the strongly focused vector, initial fused vector and source image vectors in joint sparse domain thereby preserving the edges and other critical information needed for diagnosis. This thesis demonstrates the effectiveness of the proposed algorithms when experimented on different images and the qualitative and quantitative results are compared with some of the widely used image fusion methods.

Image recognition technology plays an important role in advanced driver assistance systems (ADAS). The objective of this study is to explore the feasibility of using heterogeneous image fusion to improve the object detection performance of the ADAS. Among the many possible combinations of image types, the fusion of infrared (IR) and visible (VIS) images has great potential because of their complementary characteristics. Most studies on image fusion assume that the images involved align themselves perfectly, which is unrealistic. We address this alignment issue in this study, review various methods of image alignment and fusion, and propose an image-fusion approach that combines alignment and fusion methods for the ADAS application. Finally, we used deep learning networks to detect pedestrian and vehicle objects before and after the image fusion. The experimental results show that the fusion of IR and VIS images can improve the object detection performance of deep-learning networks. Compared with previous studies on fusion, the proposed approach ranks top if the detection accuracy improvement and execution speed are considered as a whole. This study also found that, to use image fusion to improve the object detection accuracy of deep learning networks, it is better to use fused images directly instead of unfused VIS images as the training samples.

The availability of multiple sensors, big data repositories, and connected users provides a rich set of applications for image fusion. Fusion methods vary and their effectiveness can be compared using quantitative and qualitative approaches. Whereas the previous chapter discussed quantitative methods, this chapter describes qualitative methods. <strong>10.1 Combining Approach, Methods, and Metrics</strong> The image fusion process combines multisource images to provide comprehensive, succinct, and relevant information. There are different fusion approaches and various fusion methods, of which the quantitative metrics described in Chapter 9 can be used to measure the quality of fused images. The fused images may be efficient for computer analysis, such as fusing visible and thermal images for face recognition. In such a case, the improvement of face recognition, night vision, and biomedical performance should also be effective in increasing human decision making, which is the final assessment of the image fusion usability. The fused images for human analysis include fused and colored NV imagery (NIR and LWIR) for situation awareness, PET and MRI images for medical diagnosis and treatment, as well as THz and visual imagery for security. The action time and accuracy of identifying potential objects or risks could then be used as an evaluation of the fusion and colorization method. The fusion evaluation is based on sensors, environments, and targets (e.g., operating conditions, see Chapter 4). It is very challenging to define one universal metric or evaluation method that is suitable for all image fusion methods and applications. However, a general image quality (GIQ) evaluation is still useful for the comparison and selection of fusion algorithms. Certainly, the fusion metrics and evaluation methods must be able to reflect the advantages of a fusion method for the final application.

Summary High-resolution seismic data is the basis of fine comprehensive seismic interpretation, which lead to the key position of high-resolution seismic data processing. This paper proposes a novel method aimed at extracting the high and low frequency information by a simple and effective way. Different from the traditional spectral modeling deconvolution, this method fuses the information of different frequencies extracted from multi-order calculus to broaden frequency band. Meanwhile the constraint of desired wavelet makes the result more accurate and meaningful. Testing on practical data demonstrates it is effective and feasible.

Single image dehazing is a challenging task due to the severe degradations caused by the particles in the air. Recently, various CNN-based methods have been proposed and they have achieved promising results on some dehazing tasks. However, the existing end-to-end dehazing networks process high-frequency information and low-frequency information at the same time. Therefore, most dehazing methods cannot restore dehazed image with satisfying high-frequency details. In this paper, we propose a Frequency-aware deep Dual-path Feature enhancement Network (FDF-Net) to better restore the high-frequency information while removing the haze. To achieve this, we introduce a Dual-path Feature Enhancement (DFE) block, which contains two branches: one path is to remedy the missing spatial information from high-resolution features, and the other one is to obtain new features to increase the variety of features. We believe the dual-path architecture can help the first path to focus on the recovering the high-frequency information. Furthermore, to reserve more detailed image information from the features with larger resolution, we adopt a wavelet transform module during the downsampling process of the encoder module to directly pass the high frequency information to the next level. The extensive experiments show the superiority of the proposed model over previous methods on the benchmark datasets as well as real-world hazy images.

Principal component analysis (PCA) is a method of processing high-dimensional feature data, which can be decomposed into a set of unrelated variables called principal components. Digital holographic microscopy (DHM) is a powerful tool in the biomedical imaging for recording the amplitude and phase information of object simultaneously. Structured illumination (SI) has been introduced in DHM to improve the resolution, by which the resolution can be doubled. However, accurate phase-shifting is required to retrieve the low and high frequency information. Besides that, the aberration of imaging system makes DHM under SI cumbersome. This paper presents an algorithm based on PCA for DHM under SI. The aberration terms can be extracted from the first principal component of the exponential term of filtered hologram. Moreover, the low and high frequency information can be achieved from three images without prior knowledge of phase shift values. In synthesizing process, the spectrums are precisely shifted to the correct position in the spatial-frequency domain also based on the PCA. This paper verifies the feasibility of the PCA algorithm the experiment. It is an attractive and promising technology for DHM under SI.