Homomorphic filtering is an image enhancement technique that normalizes brightness across an image and increases contrast. It involves taking the logarithm of an image to separate it into illumination and reflectance components, applying a frequency domain filter, and then taking the exponential to reconstruct an enhanced image with reduced intensity variation and highlighted details. Homomorphic filtering has applications in areas like astronomy, medicine, security, and defense.
Basic Introduction about Image Restoration (Order Statistics Filters)
Median Filter
Max and Min Filter
MidPoint Filter
Alpha-trimmed Mean filter.
and Brief Introduction to Periodic Noise
Any Question contact kalyan.acharjya@gmail.com
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
MedicalSpatial filtering is a process by which we can alter properties of an optical image by selectively removing certain spatial frequencies that make up an object, for example, filtering video data received from satellite and space probes, or removal of raster from a television picture or scanned image. Image processing, digital images slides spatial filters. Filters are divided into two types: linear (also called convolution) and nonlinear. A convolution is an algorithm that consists of recalculating the value of a pixel based on its own pixel value and the pixel values of its neighbors weighted by the coefficients of a convolution kernel. Spatial filtering is commonly used to "clean up" the output of lasers, removing aberrations in the beam due to imperfect, dirty, or damaged optics, or due to variations in the laser gain medium itself.
Basic Introduction about Image Restoration (Order Statistics Filters)
Median Filter
Max and Min Filter
MidPoint Filter
Alpha-trimmed Mean filter.
and Brief Introduction to Periodic Noise
Any Question contact kalyan.acharjya@gmail.com
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
MedicalSpatial filtering is a process by which we can alter properties of an optical image by selectively removing certain spatial frequencies that make up an object, for example, filtering video data received from satellite and space probes, or removal of raster from a television picture or scanned image. Image processing, digital images slides spatial filters. Filters are divided into two types: linear (also called convolution) and nonlinear. A convolution is an algorithm that consists of recalculating the value of a pixel based on its own pixel value and the pixel values of its neighbors weighted by the coefficients of a convolution kernel. Spatial filtering is commonly used to "clean up" the output of lasers, removing aberrations in the beam due to imperfect, dirty, or damaged optics, or due to variations in the laser gain medium itself.
Non-Blind Deblurring Using Partial Differential Equation MethodEditor IJCATR
In this paper, a new idea for two dimensional image deblurring algorithm is introduced which uses basic concepts of PDEs... The various methods to estimate the degradation function (PSF is known in prior called non-blind deblurring) for use in restoration are observation, experimentation and mathematical modeling. Here, PDE based mathematical modeling is proposed to model the degradation and recovery process. Several restoration methods such as Weiner Filtering, Inverse Filtering [1], Constrained Least Squares, and Lucy -Richardson iteration remove the motion blur either using Fourier Transformation in frequency domain or by using optimization techniques. The main difficulty with these methods is to estimate the deviation of the restored image from the original image at individual points that is due to the mechanism of these methods as processing in frequency domain .Another method, the travelling wave de-blurring method is a approach that works in spatial domain.PDE type of observation model describes well several physical mechanisms, such as relative motion between the camera and the subject (motion blur), bad focusing (defocusing blur), or a number of other mechanisms which are well modeled by a convolution. In last PDE method is compared with the existing restoration techniques such as weiner filters, median filters [2] and the results are compared on the basis of calculated PSNR for various noises
Quality Assessment of Gray and Color Images through Image Fusion TechniqueIJEEE
. Image fusion is an emerging trend in the digital image processing to enhance images. In image fusion two or more images can be fused (combined) to obtain an enhanced image. In the present work image fusion technology has been used to enhance a given input image. Image fusion is used here to combine two images which contains complementary information.
7. WHAT IS IMAGE PROCESSING? Image processing is a form of signal processing for which the input is an image, such as photographs or frames of videos and the output can either be an image or a set of characteristics or parameters related to the image.
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9. An image can be expressed as the product of illumination and reflectance: f(x,y,z)=i(x,y,z).r(x,y,z) => g(x,y,z)=ln(f(x,y,z)) => g(x,y,z)=ln(i(x,y,z))+ln(r(x,y,z))
10. Then taking DFT: F(g(x,y,z))=F(ln(i(x,y,z)))+F(ln(r(x,y,z))) Then we will apply it to a filter. we will get F(s(x,y,z)) as o/p Then inverse DFT(IDFT) of filter output is taken. we will get, s(x,y,z)=I'(x,y,z)+r’(x,y,z) Then we will take exponential to get enhanched image s’(x,y,z)=I‘’(x,y,z).r’’(x,y,z)
11. I‘’(x,y,z), r’’(x,y,z) are the illumination and reflectance of the ‘’enhanced'' image. The illumination component tends to vary slowly across the image. The reflectance tends to vary rapidly. Therefore, by applying a frequency domain filter we can reduce intensity variation across the image while highlighting detail.
