Essay On Optical Denoising

section 4.2.3.The focal spot image obtained by deconvolving a blurred image with a sharp image

         It has been 20 years since Optical Coherence Tomography (OCT) was first introduced for the diagnosis and treatment of eye disease. I became infatuated with OCT while serving as a center director at a co-management

A hierarchical Bayesian model is developed in the inverse problem setup. The Bayesian approach contains a natural mechanism for regularization in the form of a prior distribution, and a LASSO type prior distribution is used to strongly induce sparseness. We propose a variational type algorithm by minimizing the Kullback–Leibler divergence between the true posterior distribution and a separable approximation. The proposed method is illustrated on several two-dimensional linear and nonlinear inverse problems, e.g., Cauchy problem and permeability estimation problem. The proposed method performs comparably with full Markov chain Monte Carlo (MCMC) in terms of accuracy and is computationally

Optical coherence tomography provides high-resolution imaging of the vitreoretinal interface, neurosensory retina, and subretinal space. Optical coherence tomography can be used to quantify retinal thickness, monitor macular edema, identify vitreomacular traction, and detect other forms of macular disease in patients with diabetic macular edema. 105,106 OCT helps in early diagnosis of macular oedema as it is more sensitive than clinically examination, in deciding the treatment options for DME and also

Resolution is the ability to image two separate objects and visually detect one from the other. Spatial resolution refers to the ability to image small objects that have high subject contrast, such as a bone-soft tissue interface, a breast microcalcification, or a calcified lung nodule, conventional radiography has excellent spatial resolution.

My next step in processing was deconvolution. I applied a 120 ms Predictive Deconvolution but my resulting data was poor. Due to this I decided to continue processing without applying deconvolution.

bBiomedical Engineering, Vanderbilt University, Nashville, TN USA, cInstitute of Image Science, Vanderbilt University, Nashville, TN USA, dComputer Science, Vanderbilt University, Nashville, TN USA.

Optical Coherence Tomography (OCT) is a method of imaging which uses near-infrared or optical light scattering to produce three dimensional images of a medium. Medical applications are of particular interest, since OCT allows retinal and other tissues to be imaged with high resolution.

Coherent diffraction imaging (CDI) is a type of microscopy that forms an image of an object from its

By this way, Ardestani and Nejati (2011) achieve better image for the 3-D density contrast distribution.

Quantization is a lossy step where we loose data. The DCT is a lossless procedure , the data undergoing through this procedure can be precisely recovered through the IDCT . During Quantization every coefficients in the (8×8) DCT matrix is divided by its corresponding quantization value. The quantized coefficient and the reverse quantized coefficient can be achieved by the following formula,

Owing to the imperfections of image acquisition systems, the images acquired are subject to various defects that will affect the subsequent processing. Although these defects can sometimes be corrected by adjusting the acquisition hardware, for example by increasing the number of images captured for the same scene and adopting higher quality instruments, such hardware-based solutions are time-consuming and costly. Therefore it is preferable to correct the images, after they have been acquired and digitized, by using computer programs, which are fast and relatively low-cost. For example, to remove noise, smooth filters (including linear and median filters) can be applied; to enhance contrast in low-contrast images, the image histograms can

Simple cells in the visual cortex of mammalian brains can be modeled by Gabor functions.[1][2] Thus, image analysis with Gabor filters is thought to be similar to perception in the human visual system.

in a spatially variant manner because mapping function processes for each individual pixel. Local operators characterized that more satisfactorily than global operators in terms of the detail preservation and compression ratio of the dynamic range. Also, there are two tone mapping model linear tone mapping model and nonlinear tone mapping model. Linear model does not need multiscale decomposition or segmentation of the images into binary or fractional maps, where the problems associated with layer decomposition are not found. Linear model not only interested in making the output images look natural by enhancing pixel intensity more significantly in highlights, but also satisfies high radiance similarity with the ground truth HDR images. The problem appears when using linear model that usually fails when luminance value of a local patch changes abruptly. Also, the dynamic range of the dark region will be compressed at the same rate as the bright pixels, which causes losing details in this patch. Nonlinear model synchronize with the nonlinear relationship between the physical magnitudes of effects and the perceived intensity of the effects. Nonlinear model function depends on instead of linearly compressing the contrast of a patch in an input HDR image, it adopt the local gamma correction strategy which can not only effectively compress the bright pixels but also enhance the dark ones in one hit even in challenging patches Compared with the locally linear model, nonlinear

In Image Processing mostly the initial step will be the Image acquisition and smoothing. As the input for the tool of any Image Processing technique is an image, the input image should be taken and enhanced in all the ways possible. Enhancement involves smoothing the image, grey scaling, removing the unwanted blur, differentiating the subject from background and so on. In this project, for enhancing or smoothing the image we use the median filter. The median filter is non-linear digital filtering technique where the noise reduction is the pre-processing step before going to the further processing. Because the signal is big in the case of images, we chose median filter as it can handle the larger signal and the run-time is literally less.