A VQ-based blind image restoration algorithm

PSO-Optimized Blind Image Deconvolution for Improved Detectability in Poor Visual Conditions

Abstract: Image restoration is a critical step in many vision applications. Due to the poor quality of Passive Millimeter Wave (PMMW) images, especially in marine and underwater environment, developing strong algorithms for the restoration of these images is of primary importance. In addition, little information about image degradation process, which is referred to as Point Spread Function (PSF...

Semantic-Based Image Retrial in the VQ Compressed Domain using Image Annotation Statistical Models

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...

Model-Based Design Methodology for Blind Image Restoration

Blind image restoration is a kind method of image processing by estimating both the original image and the point spread function from degraded images, when there is unknown or not completely know the prior knowledge of the original image and point spread function of imaging. However, blind image restoration is a complex and intensive computing process, and increasing processing speed has been a...

Bayesian VQ Image Filtering Design with Fast Adaption Competitive Neural Networks

Vector Quantization (VQ) is a well known technique for signal compression and codification. In this paper we propose the filtering of images based on the codebooks obtained from Vector Quantization design algorithms under a Bayesian framework. The Bayesian VQ filter consists in the substitution of the image pixel by the central pixel of the codevector that encodes the pixel and its neighborhood...