A CNN Denoiser using Image Restoration

Image Restoration Using A PDE-Based Approach

 Image restoration is an essential preprocessing step for many image analysis applications. In any image restoration techniques, keeping structure of the image unchanged is very important. Such structure in an image often corresponds to the region discontinuities and edges. The techniques based on partial differential equations, such as the heat equations, are receiving considerable attention i...

Using a Cnn Sensor for 1 D Image Fixation

We describe the use of a analog VLSI vision sensor for fixation of a 1D image. The sensor, which contains processing circuitry to con-volve the image with a Gabor-type filter, is mounted on a mobile robot. Experimental results indicate that a simple proportional feedback loop using only two outputs of the sensor to command the velocity of the robot can successfully fixate a moving image on the ...

Image Restoration using Autoencoding Priors

We propose to leverage denoising autoencoder networks as priors to address image restoration problems. We build on the key observation that the output of an optimal denoising autoencoder is a local mean of the true data density, and the autoencoder error (the difference between the output and input of the trained autoencoder) is a mean shift vector. We use the magnitude of this mean shift vecto...

Image Restoration using Bregman Iteration

The digital image processing refers to processing of two dimensional images by digital computer. The image restoration is the reconstruction process applied to the degraded images. The removal of noise from images can be done by filters. New techniques are involved in the field of restoration other than filters. Nonlocal Image representation has shown great potential in various low-level vision...

Image Compression by Cnn