165 research outputs found
FPGA based implementation of low complex adaptive speckle suppression filter for B-mode medical ultrasound images
Speckles are considered as noise, which masks the fine information present in B-mode ultrasound images. Speckles appears as small snakes and dense granular like structures which has serious impact on visual perception of an image. Adaptive filter based on local statistics of an image is used to enhance the image by suppressing the noise. Adaptive speckle suppression filter enhance the image by reducing the variance between intrapixel intensities in homogeneous regions and preserving variance across interpixel intensities across the nonhomogeneous regions. In this paper, we implemented low complex adaptive speckle suppression filter on FPGA based kintex7 board. The performance of the filter is evaluated by plotting the pixel variations of original image with filtered image of an ultrasound phantom. The results show that proposed algorithm can be implemented on mobile ultrasound platforms due to 50% less computations needed per pixel compared to traditional adaptive speckle suppression algorithms, which aids better diagnosis for healthcare
Reducing adaptive optics latency using many-core processors
Atmospheric turbulence reduces the achievable resolution of ground based optical
telescopes. Adaptive optics systems attempt to mitigate the impact of this turbulence
and are required to update their corrections quickly and deterministically (i.e. in realtime).
The technological challenges faced by the future extremely large telescopes
(ELTs) and their associated instruments are considerable. A simple extrapolation of
current systems to the ELT scale is not sufficient.
My thesis work consisted in the identification and examination of new many-core
technologies for accelerating the adaptive optics real-time control loop. I investigated
the Mellanox TILE-Gx36 and the Intel Xeon Phi (5110p). The TILE-Gx36 with
4x10 GbE ports and 36 processing cores is a good candidate for fast computation of
the wavefront sensor images. The Intel Xeon Phi with 60 processing cores and high
memory bandwidth is particularly well suited for the acceleration of the wavefront
reconstruction.
Through extensive testing I have shown that the TILE-Gx can provide the performance
required for the wavefront processing units of the ELT first light instruments.
The Intel Xeon Phi (Knights Corner) while providing good overall performance does
not have the required determinism. We believe that the next generation of Xeon Phi
(Knights Landing) will provide the necessary determinism and increased performance.
In this thesis, we show that by using currently available novel many-core processors
it is possible to reach the performance required for ELT instruments
Image Restoration for Remote Sensing: Overview and Toolbox
Remote sensing provides valuable information about objects or areas from a
distance in either active (e.g., RADAR and LiDAR) or passive (e.g.,
multispectral and hyperspectral) modes. The quality of data acquired by
remotely sensed imaging sensors (both active and passive) is often degraded by
a variety of noise types and artifacts. Image restoration, which is a vibrant
field of research in the remote sensing community, is the task of recovering
the true unknown image from the degraded observed image. Each imaging sensor
induces unique noise types and artifacts into the observed image. This fact has
led to the expansion of restoration techniques in different paths according to
each sensor type. This review paper brings together the advances of image
restoration techniques with particular focuses on synthetic aperture radar and
hyperspectral images as the most active sub-fields of image restoration in the
remote sensing community. We, therefore, provide a comprehensive,
discipline-specific starting point for researchers at different levels (i.e.,
students, researchers, and senior researchers) willing to investigate the
vibrant topic of data restoration by supplying sufficient detail and
references. Additionally, this review paper accompanies a toolbox to provide a
platform to encourage interested students and researchers in the field to
further explore the restoration techniques and fast-forward the community. The
toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS
Real-Time Background Oriented Schlieren: Catching Up With Knife Edge Schlieren
Background Oriented Schlieren (BOS) is a widely used technique that provides density gradient information in flow fields of interest, without imposing stringent optical quality requirements on the facility/experiment windows and/or optics used in the BOS setup. Typically, the BOS reference image is acquired before the test begins (flow off) and then the "live" image data are acquired during the actual testing/experiment (flow on). The raw BOS image data, while displayed in real-time as they are acquired from the camera, unfortunately provide little if any visual indication of the density gradients in the flow. Generally, the "live" images must be processed off-line after the testing is completed, providing no indication of the success of the BOS setup and no feedback on the operational success of the test. Advances in computer processing hardware enables the implementation of real-time processing and display of the BOS image data. Two different approaches to implementing the real-time BOS (RT-BOS) processing capability are described herein. First, a traditional multi-core Central Processing Unit (CPU) based approach using scheduled parallel threads is used to build a RT-BOS processing engine. In the second approach, a Graphical Processing Unit (GPU) approach is used to costruct a RT-BOS processing engine. Generally, high core count CPU processors can provide a useful processing rate for RT-BOS. However, the GPU based approach exceeds the processing capability of the CPU approach, at a fraction of the cost. The GPU approach places no restrictions on the Host PC processing capability, except that it be capable of acquiring the BOS image data from the camera in real-time
FPGA based secure and noiseless image transmission using LEA and optimized bilateral filter
In today’s world, the transmission of secured and noiseless image is a difficult task. Therefore, effective strategies are important to secure the data or secret image from the attackers. Besides, denoising approaches are important to obtain noise-free images. For this, an effective crypto-steganography method based on Lightweight Encryption Algorithm (LEA) and Modified Least Significant Bit (MLSB) method for secured transmission is proposed. Moreover, a bilateral filter-based Whale Optimization Algorithm (WOA) is used for image denoising. Before image transmission, the secret image is encrypted by the LEA algorithm and embedded into the cover image using Discrete Wavelet Transform (DWT) and MLSB technique. After the image transmission, the extraction process is performed to recover the secret image. Finally, a bilateral filter-WOA is used to remove the noise from the secret image. The Verilog code for the proposed model is designed and simulated in Xilinx software. Finally, the simulation results show that the proposed filtering technique has superior performance than conventional bilateral filter and Gaussian filter in terms of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM)
Techniques for enhancing digital images
The images obtain from either research studies or optical instruments are
often corrupted with noise. Image denoising involves the manipulation of image
data to produce a visually high quality image. This thesis reviews the existing
denoising algorithms and the filtering approaches available for enhancing images
and/or data transmission.
Spatial-domain and Transform-domain digital image filtering algorithms
have been used in the past to suppress different noise models. The different noise
models can be either additive or multiplicative. Selection of the denoising algorithm
is application dependent. It is necessary to have knowledge about the noise present
in the image so as to select the appropriated denoising algorithm. Noise models
may include Gaussian noise, Salt and Pepper noise, Speckle noise and Brownian
noise. The Wavelet Transform is similar to the Fourier transform with a completely
different merit function. The main difference between Wavelet transform and
Fourier transform is that, in the Wavelet Transform, Wavelets are localized in both
time and frequency. In the standard Fourier Transform, Wavelets are only localized
in frequency. Wavelet analysis consists of breaking up the signal into shifted and
scales versions of the original (or mother) Wavelet. The Wiener Filter (mean
squared estimation error) finds implementations as a LMS filter (least mean
squares), RLS filter (recursive least squares), or Kalman filter.
Quantitative measure (metrics) of the comparison of the denoising algorithms
is provided by calculating the Peak Signal to Noise Ratio (PSNR), the Mean Square
Error (MSE) value and the Mean Absolute Error (MAE) evaluation factors. A
combination of metrics including the PSNR, MSE, and MAE are often required to
clearly assess the model performance
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