Coefficient-random-permutation-based compressive sensing method and system for image coding

Abstract

The invention relates to a coefficient-random-permutation-based compressive sensing method for image coding, which comprises the following steps of: 1) blocking an original image, and performing block-based sparse conversion; 2), reorganizing block sparse conversion coefficients obtained by the conversion of the step 1 according to positions to obtain coefficient vectors corresponding to different positions; 3), performing an intra-group coefficient random permutation operation on each coefficient vector obtained by the step 2 respectively; 4), sequentially extracting a coefficient from each coefficient vector permutated by the step 3 to form the coefficient vectors of which sizes are the same as those of blocks of the original image and which correspond to the positions; 5), performing compressed sampling code expression on each coefficient vector produced by the step 4); and 6), reconstructing the original image by an inverse process of the process. By the method, the measurement rate of image compressed sensing can be remarkably reduced, and the quality of the reconstructed image can be improved.

Description

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AI Technical Summary

Benefits of technology

This patented technique uses techniques like histogram transformation for dividing images into smaller blocks called pixels (block), which helps recover higher levels of energy from lower values than usual by replacing certain parts of those areas where we have high activity without affecting others' activities. By doing this, it allows us to make better use of available resources while still maintaining accurate results during imagery processing tasks such as medical diagnosis.

Problems solved by technology

Technological Problem addressed in this patents relates to improving the efficiency of decompression and recording techniques applied to compressive imagery systems like digital cameras. Specifically, current methods require complex mathematical models and may result in significant errors when extracting certain types of samples due to their nonlinear nature. To address these issues, some approaches propose learning dictionaries instead of classical ones, including neural networks and convolution algorithms. These solutions aim to learn representative representations of specific areas within the input space through training sequences, allowing for flexible allocation of measurement gains between different blocks.

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