Thus, using a (j − 2)-level decomposition for the MS image
and a j-level decomposition for the PAN image may result
in a slight drift between the edges in the two representations and thus double edges will appear in the pansharpened image after reconstruction. This problem does not appear in the orig- inal wavelet-based methods, since the same level of wavelet decomposition is applied on both input images and possible correction to edge localisation is performed via registration after upsampling. In the proposed scheme this problem can be alleviated by using a wavelet family with strong edge localisation properties, so that that the wavelet decomposition introduces minimal shift to the original position of the edges. The Biorthogonal Spline Wavelets are shown to provide ac- curate edge localisation along the wavelet decomposition, as they provide optimal spatial-frequency localisation . In this analysis, the Toolbox Wavelets’ implementation of Biorthogo- nal Spline Wavelets (Cohen-Daubechies-Feauveau CDF) (3,9) is employed to perform the wavelet decomposition . Using the aforementioned wavelet family, one can encounter minimal edge localisation problems in the pansharpened image, without the extra registration and upsampling stages.
This paper describes the application of a simple and fast pixel-by-pixel atmospheric correction method to high spatial resolution images. This application results from the preliminary ongoing attempts for the monitoring of the sea breaking zone in the northwest coast of Portugal using high spatial resolution satellite images. The low reflectance values of sea water in the visible and near infrared spectral regions make atmospheric correction an essential processing task, as most of the signal recorded by the sensor is due to the atmosphere. The method relies on a simplified use of the 6S RTC without recourse to multidimensional look-up tables (LUT). It can be used for both present and past data and is also suited for near real time applications. An estimation of the surface (or Bottom Of Atmosphere, BOA) reflectance is made from the signal recorded by the satellite sensor at the Top Of Atmosphere (TOA). The input information required includes a set of ground horizontal visibility values at 0.550µm, and the observation / illumination geometry and target height for each pixel. The ground horizontal visibility values are used by the RTC to estimate the aerosol loading, for a given atmospheric scenario.
Digital Object Identifier 10.1109/LSP.2016.2608858
resolution compared to MS and PAN images (that are acquired in much fewer bands). As a consequence, reconstructing a high- spatial and high-spectral multiband image from two degraded and complementary observed images is a challenging but cru- cial issue that has been addressed in various scenarios –. In particular, fusing a high-spatial low-spectral resolution image and a low-spatial high-spectral image is an archetypal instance of multiband image reconstruction, such as pansharpening  or HS pansharpening . Generally, the linear degradations ap- plied to the observed images with respect to (w.r.t.) the target high-spatial and high-spectral image reduce to spatial and spec- tral transformations. Thus, the multiband image fusion problem can be interpreted as restoring a three-dimensional data-cube from two degraded data-cubes. A detailed formulation is pre- sented below.
The employed objectives can be stated as follows: firstly, image preprocessing steps were handled to extract and enhance the hand images. These preprocessings were eliminated or cropped, morphological operations, adding top-hat characteristics and an unsharp filter. Secondly, a feature fusion between multi-spectralimages to combine the features, where Haar wavelet fusion based on the mean rule was used. Thirdly, a wavelet transform was applied for the enhanced and fused image. Fourthly, the MLP neural networks were trained by considering a right hand to predict the inner face image and the left hand to predict the outer face image. Fifthly, a score fusion was utilized to collect the face image according to the maximum or adding rule. Sixthly, the same processings should be followed to test the MLPs using different data. Finally, the last decision was taken and hence, Figures 1 and 2 demonstrate the block diagrams for our proposed method. This new suggested topology will increase the security and effictiveness of the biometric system.
provided by three sensors covering the red, green and blue spectral wavelengths. These sensors have a low number of pixels (low spatial resolution) and the small objects and details (cars, small lines, etc.) are hidden. Such small objects and details can be observed with a different sensor (panchromatic), which have a high number of pixels (high spatial resolution) but without color information. With a fusion process a unique image can be achieved containing both: high spatial resolution and color information . There are two approaches to image fusion, namely Spatial Fusion (SF) and Transform fusion (TF). In Spatial fusion, the pixel values from the source images are summed up and taken average to form the pixel of the composite image at that location . Image fusion methods based on Multiscale Transforms (MST) are a popular choice in recent research. MST fusion uses Pyramid Transform (PT) or Discrete Wavelet Transform (DWT) for representing the source image at multi scale. PT methods construct a fused pyramid representation from the pyramid representations of the original images. The fused image is then obtained by taking an inverse PT . Due to the disadvantages of PT, which include blocking effects and lack of flexibility, approaches based on DWT have begun . In , DWT approach is considered and it uses area level maximum selection rule and a consistency verification step. But, DWT suffers from lack of shift invariance and poor directionality. One way to avoid these disadvantages is to use Dual Tree Complex Wavelet Transform (DTCWT), which is most expensive, computationally intensive, and approximately shift invariant [6-13]. But, the un-decimated DWT, namely Stationary Wavelet Transform (SWT) is shift invariant and Wavelet Packet Transform (WPT) provides more directionality. This benefit comes from the ability of the WPT to better represent high frequency content and high frequency oscillating signals in particular. The MultiWavelet Transform (MWT) of image signals produces a non-redundant image representation, which provides better spatial and spectral localization of image formation than DWT. This paper presents the performance of Multi-Stationary Wavelet Packet Transform in multi-focused image fusion in terms of Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE), Quality Index (QI) and Normalized Weighted Performance Metric (NWPM).
For example, in multi-focus imaging one or more objects may be in-focus in a particular image, while other objects in the scene may be in focus in other images. For remotely sensed images, some have good spectral information whereas others have high geometric resolution. In the arena of biomedical imaging, two widely used modalities, namely the magnetic resonance imaging (MRI) and the computed tomography (CT) scan do not reveal identically every detail of brain structure. While CT scan is especially suitable for imaging bone structure and hard tissues, the MR images are much superior in depicting the soft tissues in the brain that play very important roles in detecting diseases affecting the skull base. These images are thus complementary in many ways and no single image is totally sufficient in terms of their respective information content. The advantages these images may be fully exploited by integrating the complementary features seen in different images through the process of image fusion that generates an image composed of features that are best detected or represented in the individual images. Important applications of the fusion of images include medical imaging, microscopic imaging, remote sensing, computer vision, and robotics.
CS-based techniques have the main advantages of a high spa- tial sharpness of the enhanced image, a fast and easy imple- mentation, and robustness to misregistration errors and alias- ing . On the other hand, MRA methods have the relevant advantage to preserve the spectral information of the original image. However, when these approaches are considered ade- quate if applied to MS and PAN images, they may have sev- eral drawbacks, in terms of spectral distortion and increase of computational time, when the low-resolution image is a HS image. In particular, due to the different spectral coverage between the PAN and the HS images, CS methods result in enhanced images affected by strong spectral distortions. In general, these approaches are considered adequate if applied to MS and PAN images, they may have several drawbacks, in terms of spectral distortion and increase of computational time, when the low-resolution image is a HS image. In partic- ular, CS methods present a fast and easy implementation and permit to obtain enhanced images having high spatial quality. However, due to the substitution process, CS methods usually introduce spectral distortions . On the other hand, the im- ages obtained using MRA approaches are not usually as sharp as the ones obtained following CS methods but are spectrally consistent with the original images . From this point of view, one of the main challenges for fusing low-resolution HS and high-resolution PAN data is to find an appropriate balance between spectral and spatial preservation. A possible solution to overcome mutual limitations of both CS and MRA approaches, is to use hybrid approaches, combining the differ- ent classes of methods in order to find an appropriate balance between spectral and spatial preservation. Aim of this paper is to evaluate the effect of using a hybrid framework in terms of quality of the enhanced image as well as in computational time.
Received: 28 August 2019; Accepted: 24 September 2019; Published: 4 October 2019
Abstract: Pansharpening is the process of merging the spectral resolution of a multi-band remote-sensing image with the spatial resolution of a co-registered single-band panchromatic observation of the same scene. Conceived and contextualized over 30 years ago, panharpening methods have progressively become more and more sophisticated, but simultaneously they have started producing fewer and fewer reproducible results. Their recent proliferation is most likely due to the lack of standardized assessment procedures and especially to the use of non-reproducible results for benchmarking. In this paper, we focus on the reproducibility of results and propose a modified version of the popular additive wavelet luminance proportional (AWLP) method, which exhibits all the features necessary to become the ideal benchmark for pansharpening: high performance, fast algorithm, absence of any manual optimization, reproducible results for any dataset and landscape, thanks to: (i) spatial analysis filter matching the modulation transfer function (MTF) of the instrument; (ii) spectral transformation implicitly accounting for the spectral responsivity functions (SRF) of the multispectral scanner; (iii) multiplicative detail-injection model with correction of the path-radiance term introduced by the atmosphere. The revisited AWLP has been comparatively evaluated with some of the high performing methods in the literature, on three different datasets from different instruments, with both full-scale and reduced-scale assessments, and achieves the first place, on average, in the ranking of methods providing reproducible results.
A widespread alternative to the direct handling of Poisson statistics is to apply variance-stabilizing trans- forms (VSTs)—with the underlying idea of exploiting the broad class of denoising methods that are based on a Gaussian noise model  . Since the seminal work of Anscombe  , more involved VSTs have been proposed, such as the Haar–Fisz transform  . Such approaches belong to the state-of-the-art for 1D wavelet-based Poisson noise removal [2,12] . They have been combined with various other methodologies, e.g., Bayesian multi- scale likelihood models that can be applied to arbitrary wavelet transforms  . Very recently, a hybrid approach that combines VSTs, hypothesis testing, ‘ 1 -penalized reconstruction and advanced redundant multiscale repre- sentations has been proposed by Zhang et al.  .
It is helpful to use discrete wavelet transform (DWT) because of its advantages such as time-frequency local- ization, multi-rate filtering, and scale-space analysis . EEG sub-bands have more accurate information about neuronal activity compared to the original full spectrum EEG. Also, DWT is a powerful transform to analyse non-stationary signals, because it has a good localization in both time and frequency domains [4,5]. Fast events and changes in neuronal activities like spikes that are not obvious in full spectrum EEG, can be recognized in sub-bands. Thus to detect epileptic seizures accurately, each sub-band should be analysed separately .
physiological/functional knowledge from Photon Emission Tomography (PET). Image fusion can form a single composite image from different modality images of the same subject and provide complete information for further analysis and diagnosis. But it is necessary to align two images accurately before they fused. Before fusing images we should preserve all features in the images and should not introduce any artifacts or inconsistency which would distract the observer.Wavelet based fusion satisfies the requirement due to lots of advantages.
CHANGE Detection(CD) is a stand out amongst the most essential application in remote detecting innovation .The point of CD is to discover pixels that compare to genuine changes on the ground in sets of co-registered pictures obtained over the same geological region at two distinct times .Typically, CD strategies depend on the of that, the Distinction Images (DI) from two co-registered pictures, and after that, progression are distinguished via naturally dividing the DI into two areas connected with changed and unaltered(no-change) classes, separately. None the less, as these strategies are information driven. The completely programmed separation in the middle of changed and unaltered classes is compelled by the unpredictability of the measurable dispersions portraying these classes, their level of cover, and introduction. as of late , the use of self-loader strategies with client’s mediation (i.e., intelligent division) has ended up famous in the writing of picture handling . They speak to a promising answer for upgrading and summing up
Human beings are good at deriving information from such images, because of our innate visual and mental abilities. About 75% of the information received by human is in pictorial form. An image is digitized to convert it to a form which can be stored in a computer's memory or on some form of storage media such as a hard disk or CD-ROM. This digitization procedure can be done by a scanner, or by a video camera connected to a frame grabber board in a computer. Once the image has been digitized, it can be operated upon by various image processing operations. Image processing operations can be roughly divided into three major categories, Image Compression, Image Enhancement and Restoration, and Measurement Extraction. It involves reducing the amount of memory needed to store a digital image. Image defects which could be caused by the digitization process or by faults in the imaging set-up (for example, bad lighting) can be corrected using Image Enhancement techniques. Once the image is in good condition, the Measurement Extraction operations can be used to obtain useful information from the image. Some examples of Image Enhancement and Measurement Extraction are given below. The examples shown all operate on 256 grey- scale images. This means that each pixel in the image is stored as a number between 0 to 255, where 0 represents a black pixel, 255 represents a white pixel and values in-between represent shades of grey. These operations can be extended to operate on colour images. The examples below represent only a few of the many techniques available for operating on images. Details about the inner workings of the operations have not been given, but some references to books containing this information are given at the end for the interested reader As we mentioned in the preface, human beings are predominantly visual creatures: we rely heavily on our vision to make sense of the world around us. We not only look at things to identify and classify them, but we can scan for differences, and obtain an overall rough feeling for a scene with a quick glance. Humans have evolved very precise visual skills: we can identify a face in an instant; we can differentiate colors; we can process a large amount of visual information very quickly.
Many methods have been proposed in stereo matching. Unfortunately, till now no methods can be applied in the practical application yet. This field still under exploitation and new algorithms are continued to be proposed to achieve good results. All methods can be classified into largely two classes: feature-based and area-based matching.
We can say that matching of features is a search problem. Most of previous methods on stereo matching 1-4 fixed the searching range to a certain size during the whole process. Usually the images are divided into many small blocks, non- overlapped or overlapped. The search of shape similarity is performed within the corresponding block. If the matching window is too small and does not cover enough intensities variation, it gives a poor disparity estimation, because the signal to ratio is low. If the window is too large, then the position of maximum correlation may not represent the correct matching due to different projective distortions in left and right images. Furthermore, unchanged searching range may cause time- consuming problem or increase mismatch.
In this thesis, I have gone through many classic machine learning algorithms like K-means, Expectation Maximization, Hierarchical Clustering, some out of box methods like Unsupervised Artificial DNA Classifier, Spatial Spectral Information which integrates both features to get better classification and a variant of Maximal Margin Clustering which uses K-Nearest Neighbor algorithm to cross validate and get the best set to separate. Sometimes PCA is used get best features from the dataset. Finally all the results are compared.
Gabor wavelets have proven to be a very useful tool in image processing especially for the texture analysis and are widely adopted in the literature [17, 19]. They have been demonstrated to be very useful in detection of the texture direction. The major advantage of the Gabor wavelet analysis over the Fourier Transform is that it can achieve optimal direction in both spatial and frequency domain. The Gabor filters are band pass filters with tunable center frequency, orientation and bandwidth. The Gabor wavelets are a set of filters that have the ability to analyze the directional aspect of a given data. Properly tuned Gabor wavelets react strongly to specific texture and weakly to others. These basic characteristics of the Gabor wavelets served the purpose of the proposed algorithm, i.e. of exploiting the directionality of the radar images. A Gabor function is a Gaussian function modulated by an oriented complex sinusoidal signal.
Md. MahmudulHasan  presented a new reduction method named as PAPR based on linear predictive coding (LPC). The crucial issue in orthogonal frequency division multiplexing (OFDM) was the high peak-to-average power ratio (PAPR) that results in severe nonlinear distortion in real hardware executions of huge power amplifier. This technique has introduced the use of signal whitening property of LPC in OFDM systems as a preprocessing step. Error filtering technique mentioned in the proposed method extracts the expectable content of stationary stochastic processes which can diminish the autocorrelation of input data sequences and was shown to be very powerful solution for PAPR issue in OFDM transmissions. It can be viewed that our new approach can obtain a powerful reduction scheme in PAPR without reducing the power of spectral level, error performance or overall computational complexity of the systems. It was also proved that the proposed method was a stand-alone method of all the modulation schemes and can be applied to any number of subcarriers under both additive white Gaussian noise and wireless Rayleigh fading channel.
Previous techniques of thresholding includes filtering in spatial domain, however, in wavelets, the complete analysis is shifted from spatial domain to frequency domain having both time –scale aspects. Wavelet transform (WT) represents an image as a sum of wavelet functions (wavelets) with different locations and scales. Any decomposition of an image into wavelets involves a pair of waveforms: one to represent the high frequencies corresponding to the detailed parts of an image (wavelet function ψ) and one for the low frequencies or smooth parts of an image (scaling function Ø). The Discrete wavelet transform (DWT) has gained wide popularity due to its excellent decorrelation property, many modern image and video compression systems embody the DWT as the transform stage . It is widely recognized that the 9/7 filters are among the best filters for DWT-based image compression. In fact, the JPEG2000 image coding standard employs the 9/7 filters as the default wavelet filters for lossy compression and 5/3 filters for lossless compression. The performance of a hardware implementation of the 9/7 filter bank (FB) depends on the accuracy with which filter coefficients are represented. Lossless image compression techniques find applications in fields such as medical imaging, preservation of artwork, remote sensing etc  . Day-by- day Discrete Wavelet Transform (DWT) is becoming more and more popular
Coif lets are discrete wavelets developed by Ronald Coifman. These wavelets are symmetric with wavelet function . They have N/3 vanishing moments and scaling functions of N/3-1, which were employed in a great space of problems. The Coif wavelet and the famous Daub wavelet are equally powerful in some aspects, but the Coif wavelet was developed having vanishing moments supporting both wavelet function φ(x) and scaling function ø(x). It has N/3 numbers of vanishing moments and N/3 – 1 numbers of scaling functions.