Satellite Image Fusion in Various Domains

In order to find out the fusion algorithm which is best suited for the panchromatic and multispectral images, fusion algorithms, such as PCA and wavelet algorithms have been employed and analyzed. In this paper, performance evaluation criteria are also used for quantitative assessment of the fusion performance. The spectral quality of fused images is evaluated by the ERGAS and Q4. The analysis indicates that the DWT fusion scheme has the best definition as well as spectral fidelity, and has better performance with regard to the high textural information absorption. Therefore, as the study area is concerned, it is most suited for the panchromatic and multispectral image fusion. an image fusion algorithm based on wavelet transform is proposed for Multispectral and panchromatic satellite image by using fusion in spatial and transform domains. In the proposed scheme, the images to be processed are decomposed into sub-images with the same resolution at same levels and different resolution at different levels and then the information fusion is performed using high-frequency sub-images under the Multi-resolution image fusion scheme based on wavelets produces better fused image than that by the MS or WA schemes

Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments

The localized nature of curvelet functions, together with their frequency and dip characteristics, makes the curvelet transform an excellent choice for processing seismic data. In this work, a denoising method is proposed based on a combination of the curvelet transform and a whitening filter along with procedure for noise variance estimation. The whitening filter is added to get the best performance of the curvelet transform under coherent and incoherent correlated noise cases, and furthermore, it simplifies the noise estimation method and makes it easy to use the standard threshold methodology without digging into the curvelet domain. The proposed method is tested on pseudo-synthetic data by adding noise to real noise-less data set of the Netherlands offshore F3 block and on the field data set from east Texas, USA, containing ground roll noise. Our experimental results show that the proposed algorithm can achieve the best results under all types of noises (incoherent or uncorrelated or random, and coherent noise)

Pixel-level Image Fusion Algorithms for Multi-camera Imaging System

This thesis work is motivated by the potential and promise of image fusion technologies in the multi sensor image fusion system and applications. With specific focus on pixel level image fusion, the process after the image registration is processed, we develop graphic user interface for multi-sensor image fusion software using Microsoft visual studio and Microsoft Foundation Class library. In this thesis, we proposed and presented some image fusion algorithms with low computational cost, based upon spatial mixture analysis. The segment weighted average image fusion combines several low spatial resolution data source from different sensors to create high resolution and large size of fused image. This research includes developing a segment-based step, based upon stepwise divide and combine process. In the second stage of the process, the linear interpolation optimization is used to sharpen the image resolution. Implementation of these image fusion algorithms are completed based on the graphic user interface we developed. Multiple sensor image fusion is easily accommodated by the algorithm, and the results are demonstrated at multiple scales. By using quantitative estimation such as mutual information, we obtain the experiment quantifiable results. We also use the image morphing technique to generate fused image sequence, to simulate the results of image fusion. While deploying our pixel level image fusion algorithm approaches, we observe several challenges from the popular image fusion methods. While high computational cost and complex processing steps of image fusion algorithms provide accurate fused results, they also makes it hard to become deployed in system and applications that require real-time feedback, high flexibility and low computation abilit

Analisis Pengaruh Citra Gelap, Normal, Terang Terhadap Wavelet Orthogonal

Abstract. An image is classified into dark, normal, and bright image. The images are grouped in the dark images according to the histogram and the mu value. An image consists of information and redundancies. The use of wavelet is considered effective in image compression and it does not only cut down the memory usage but also it makes devices work faster. In this study, an analysis in conducted on the influence of dark, normal, and bright images on the orthogonal wavelet. Peak Signal to Noise Ratio (PSNR) is used to compare 17 functions of wavelet orthogonal in the image compression of dark, normal, and bright images. PSNR is a measurement parameter commonly used for measuring the quality of image reconstruction which is then compared with the original image. Compression ratio is used to measure the reduction of the data size after the compression process. Based on the research on the dark, normal, and bright image, the findings reveal that bright image has got the lowest PNSR value at all image testing while the normal image has the highest PSNR value at the wavelet orthogonal application. Keywords : Image compression, Orthogonal wavelet, PSNR, compression ratio.Abstrak. Suatu citra dikelompokkan menjadi citra gelap, citra normal, dan citra terang. Pengelompokan citra menjadi warna gelap terlihat dari histogram dan nilai rerata intensitas (mu). Citra terdiri atas informasi dan redudansi. Penggunaan wavelet dinilai efektif dalam kompresi citra dan menurunkan penggunaan memori serta membuat perangkat menjadi lebih cepat. Pada penelitian ini, dilakukan analisis pengaruh citra gelap, citra normal, dan citra terang terhadap wavelet orthogonal. Peak Signal to Noise Ratio (PSNR) digunakan untuk membandingkan 17 fungsi wavelet orthogonal dalam kompresi citra gelap, citra normal, dan citra terang. PSNR adalah parameter ukur yang sering digunakan untuk pengukuran kualitas gambar rekonstruksi, yang lalu dibandingkan dengan gambar asli. Rasio kompresi digunakan untuk mengukur pengurangan ukuran data setelah proses kompresi. Berdasarkan penelitian pada citra gelap, citra normal, dan citra terang diperoleh bahwa citra terang menghasilkan nilai PSNR paling kecil untuk seluruh citra uji dan citra normal menghasilkan nilai PSNR paling besar dalam penerapan wavelet orthogonal. Kata kunci : Kompresi citra, Wavelet orthogonal, PSNR, rasio kompresi

Detail and contrast enhancement in images using dithering and fusion

This thesis focuses on two applications of wavelet transforms to achieve image enhancement. One of the applications is image fusion and the other one is image dithering. Firstly, to improve the quality of a fused image, an image fusion technique based on transform domain has been proposed as a part of this research. The proposed fusion technique has also been extended to reduce temporal redundancy associated with the processing. Experimental results show better performance of the proposed methods over other methods. In addition, achievements have been made in terms of enhancing image contrast, capturing more image details and efficiency in processing time when compared to existing methods. Secondly, of all the present image dithering methods, error diffusion-based dithering is the most widely used and explored. Error diffusion, despite its great success, has been lacking in image enhancement aspects because of the softening effects caused by this method. To compensate for the softening effects, wavelet-based dithering was introduced. Although wavelet-based dithering worked well in removing the softening effects, as the method is based on discrete wavelet transform, it lacked in aspects like poor directionality and shift invariance, which are responsible for making the resultant images look sharp and crisp. Hence, a new method named complex wavelet-based dithering has been introduced as part of this research to compensate for the softening effects. Image processed by the proposed method emphasises more on details and exhibits better contrast characteristics in comparison to the existing methods

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Curvelets and Ridgelets

International audienceDespite the fact that wavelets have had a wide impact in image processing, they fail to efficiently represent objects with highly anisotropic elements such as lines or curvilinear structures (e.g. edges). The reason is that wavelets are non-geometrical and do not exploit the regularity of the edge curve. The Ridgelet and the Curvelet [3, 4] transforms were developed as an answer to the weakness of the separable wavelet transform in sparsely representing what appears to be simple building atoms in an image, that is lines, curves and edges. Curvelets and ridgelets take the form of basis elements which exhibit high directional sensitivity and are highly anisotropic [5, 6, 7, 8]. These very recent geometric image representations are built upon ideas of multiscale analysis and geometry. They have had an important success in a wide range of image processing applications including denoising [8, 9, 10], deconvolution [11, 12], contrast enhancement [13], texture analysis [14, 15], detection [16], watermarking [17], component separation [18], inpainting [19, 20] or blind source separation[21, 22]. Curvelets have also proven useful in diverse fields beyond the traditional image processing application. Let’s cite for example seismic imaging [10, 23, 24], astronomical imaging [25, 26, 27], scientific computing and analysis of partial differential equations [28, 29]. Another reason for the success of ridgelets and curvelets is the availability of fast transform algorithms which are available in non-commercial software packages following the philosophy of reproducible research, see [30, 31]

Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing

Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to process and analyze these hyperspectral (HS) products mainly by means of seasoned experts. However, with the ever-growing volume of data, the bulk of costs in manpower and material resources poses new challenges on reducing the burden of manual labor and improving efficiency. For this reason, it is, therefore, urgent to develop more intelligent and automatic approaches for various HS RS applications. Machine learning (ML) tools with convex optimization have successfully undertaken the tasks of numerous artificial intelligence (AI)-related applications. However, their ability in handling complex practical problems remains limited, particularly for HS data, due to the effects of various spectral variabilities in the process of HS imaging and the complexity and redundancy of higher dimensional HS signals. Compared to the convex models, non-convex modeling, which is capable of characterizing more complex real scenes and providing the model interpretability technically and theoretically, has been proven to be a feasible solution to reduce the gap between challenging HS vision tasks and currently advanced intelligent data processing models

Bayesian Fusion of Multi-Band Images

International audienceThis paper presents a Bayesian fusion technique for remotely sensed multi-band images. The observed images are related to the high spectral and high spatial resolution image to be recovered through physical degradations, e.g., spatial and spectral blurring and/or subsampling defined by the sensor characteristics. The fusion problem is formulated within a Bayesian estimation framework. An appropriate prior distribution exploiting geometrical considerations is introduced. To compute the Bayesian estimator of the scene of interest from its posterior distribution, a Markov chain Monte Carlo algorithm is designed to generate samples asymptotically distributed according to the target distribution. To efficiently sample from this high-dimension distribution, a Hamiltonian Monte Carlo step is introduced within a Gibbs sampling strategy. The efficiency of the proposed fusion method is evaluated with respect to several state-of-the-art fusion techniques