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from, say, 0 (completely white) to 256 (completely black). Put together, this data constitutes the picture. Thus,
mathematically a photo consists of a sequence of pairs of numbers, namely, a numbering of the pixels together
with the associated light intensity. Curvelet Transform consisted of special filtering process and multi-scale
Ridgelet Transform. It could fit image properties well. Nevertheless, Curvelet Transform had complicated digital
realization, includes sub-band division, smoothing block, normalization, Ridgelet analysis and so on. Curvelet‟s
pyramid decomposition brought immense data redundancy [10]. Forward Fast Curvelet Transform (FCT) was the
Second Generation Curvelet Transform which was simpler and easily understandable. On the other hand,
Wavelets are frequently used to remove noise from music recordings. The main idea is to think about a music
signal as consisting of the music itself to which some noise is added. The music signal itself describes how the
music changes in time; we can think about the signal as the current through the loudspeaker when we play a
recording. The noise contribution is usually small compared to the music, but irritating for the ears; it also
contributes to the coefficients, but usually less than the music itself. The idea is now to remove the coefficients
which are smaller than a certain threshold value (this procedure is not applied on the signal itself, but on its so
called wavelet transform).
A special member of the emerging family of multiscale geometric transforms is the curvelet
transform, which was developed in the last few years in an attempt to overcome inherent limitations of traditional
multiscale representations such as wavelets [11]. Conceptually, the curvelet transform is a multiscale pyramid
with many directions and positions at each length scale, and needle-shaped elements at fine scales. This pyramid
is nonstandard, however. Indeed, curvelets have useful geometric features that set them apart from wavelets and
the likes. For instance, curvelets obey a parabolic scaling relation which says that at scale 2−j
, each element has an
envelope which is aligned along a “ridge” of length 2−j/2
and width 2−j
. Fast Discrete Curvelet Transforms (FDCT)
describes two digital implementations of a new mathematical transform, namely, the second generation curvelet
transform in two and three dimensions. The first digital transformation is based on unequally-spaced fast Fourier
transforms (USFFT) while the second is based on the wrapping of specially selected Fourier samples. The two
implementations essentially differ by the choice of spatial grid used to translate curvelets at each scale and angle.
Both digital transformations return a table of digital curvelet coefficients indexed by a scale parameter, an
orientation parameter, and a spatial location parameter [9]. Both implementations are fast in the sense that they
run in O(n2
log n) flops for n by n Cartesian arrays; in addition, they are also invertible, with rapid inversion
algorithms of about the same complexity. The digital transformations improve upon earlier implementations based
upon the first generation of curvelets, in the sense that they are conceptually simpler, faster and far less redundant.
The next section introduces the concept, design and development of Crooklet Transform.
2. CONTINUOUS CROOKLET TRANSFORM (CCrT)
In definition, the continuous crooklet transform is a convolution of the input data sequence with
a set of functions generated by the principal crooklet. The convolution can be computed by using a Fast Fourier
Transform (FFT) algorithm. Normally, the output is a real valued function except when the principal crooklet is
complex. A complex principal crooklet will convert the continuous crooklet transform to a complex valued
function. The power spectrum of the continuous crooklet transform can be represented by | ( )| . The
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3. DISCRETE CROOKLET TRANSFORM (DCrT)
The DCrT of a signal is given by;
∑
DCrT is calculated by passing through a series of filters. First the samples are passed through a low pass filter
with impulse response resulting in a convolution of the two:
( )
∑ ( )
The signal is also decomposed simultaneously using a high-pass filter . The outputs give the
detail coefficients (from the high-pass filter) and approximation coefficients (from the low-pass). It is important
that the two filters are related to each other and they are known as square root mirror filter.
( )
∑ ( )
This decomposition has doubled the time resolution since only square root of each filter output
characterizes the signal series. However, each output has double the frequency band of the input, so the frequency
resolution has been squared similar to in curvelet transform.
Time complexity
The filter bank implementation of the Discrete Crooklet Transform (DCrT) takes only O(N), as
compared to O(N log N) for the fast Fourier transform. The filter bank does each of these two O(N) convolutions.
4. APPLICATIONS OF CROOKLET TRANSFORM
Crooklet transform is a powerful statistical tool which can be used for a wide range of
applications, namely: Signal processing, Data compression, Smoothing and image denoising, Fingerprint
verification, Biology for cell membrane recognition, To distinguish the normal from the pathological membranes,
DNA analysis, Protein analysis, Blood-pressure, Heart-rate and ECG analysis, Finance for detecting the properties
of quick variation of values, In Internet traffic description, For designing the services size, Industrial supervision
of gear-wheel, Speech recognition, Computer graphics and multifractal analysis, and In many areas of physics
(molecular dynamics, astrophysics, etc.). The applications of the Crooklet transform were extended to image
contrast enhancement and astronomical image representation to the fusion of satellite images. The applications of
Crooklets showed continuous improvement in many fields involving image/video presentation, denoising and
classification. The first model introduced in Euro Robotics 2019, Crooklet Transform lay the fundamental block
in building self-learning robots since their computational cost is significantly lower. Crooklet transform overlooks
the challenge of space-domain formulation of curvelet transform [10].
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d) Face Recognition:
Face recognition such as identification of person using Credit cards, Passport check, Criminal
investigations etc. The human face is an important object in image and video databases, because it is a unique
feature of human beings and is ubiquitous in photos, news videos, and video telephony. Face detection can be
regarded as a more general case of face localization. In face localization, the task is to find the locations and sizes
of a known number of faces (usually one). In face detection, one does not have this additional information. There
are several application areas where automated face recognition is a relatively new concept. Developed in the
1960s, the first semi-automated system for face recognition required the administrator to locate features (such as
eyes, ears, nose, and mouth) on the photographs before it calculated distances and ratios to a common reference
point, which were then compared to reference data [5]. Face recognition can be used for both verification and
identification (open-set and closed-set). Linear Discriminant Analysis (LDA) with Crooklet transformation can be
regarded as one of the principal techniques for face recognition systems. LDA is well-known arrangement for
feature extraction and dimension reduction. LDA using Crooklet Transform approach enhances performance as
regards accuracy and time complexity.
e) Image Fusion:
Extracting more information from multi-source images is an attractive thing in remotely sensed
image processing, which is recently called the image fusion [8]. Image Fusion using Crooklet Transform is used
to improve the geometric resolution of the images, in which two images to be processed are firstly decomposed
into sub-images with different frequency and then the information fusion is performed using these images under
certain conditions and finally these sub-images are reconstructed into the result image with plentiful information.
The successful fusion of images acquired from different modalities or Instruments is of great importance in many
applications such as medical imaging, microscopic imaging, remote sensing, computer vision, and robotics [2].
For the remotely-sensed images, some have good spectral information, and the others have high geometric
resolution, how to integrate the information of these two kinds of images into one kind of images is very attractive
thing in image processing, which is called image fusion. For the purpose of realization of this task, we often need
some algorithms to fuse the information of these two kinds of images. Crooklet Transform used in the fields of
graphics and image fusion is been proved to be an effective tool to process the signals in multiscale spaces.
Some Advantages of Crooklet Transform:
One of the main advantages of Crooklet Transform is that they offer a simultaneous localization in time
and frequency domain.
The second main advantage of Crooklet Transform is that it is computationally feasible.
Crooklet Transform has the great advantage of being able to separate the fine details in a signal.
It is possible to obtain a good approximation of the given function „f‟ by using only a few coefficients
which are the great achievement in comparison to other transform.
Crooklet Transform is capable of revealing aspects of data that other signal analysis techniques miss the
aspects like trends, breakdown points, and discontinuities in higher derivatives and self-similarity.
It can efficiently compress or denoise a signal without appreciable degradation.
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CONCLUSION
Curvelet transform descends from the wavelet transform, and it overwhelms the deficits of
wavelet transform in the manifestation of image edges. The anisotropic features of curvelet transform provide an
idyllic depiction of the object edges. However, both wavelet and curvelet transforms were not fair enough to
provide efficient representation in all the domains of signal and image processing. The proposed prototype of
Continuous and Discrete Crooklet Transform is anticipated to be a powerful mathematical transformation tool in
all arenas of science, engineering, and medicine. The Crooklet Transform embraces the imperative phenomena‟s
from both the wavelet and the curvelet transforms. The incompetence and shortfalls of the major transforms were
endowed by the proposed Crooklet Transform. As the name indicates crooklets are efficient in dealing with the
contours, edges, curves and other aspects related to image and video processing as well. To instigate, the Crooklet
Transform will amateur a basis for third-generation transforms.
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