The present paper has mentioned theoretical and practical results of application of an instrument of orthogonal transformations based on basis Walsh functions for information compression under transmission of aerospace images through the communication channel into embedded cyber physical systems. Perceval equality has been shown for quasi-two-dimensional representation of two- dimensional signals. Quality of the image restoration has been evaluated depending on the compression ratio. Protocols for transmission of the formed signal have been suggested. Examples and evaluations of restored images have been indicated. In this paper, we propose a novel learning-based image restoration scheme for compressed images by suppressing compression artifacts and recovering high frequency (HF) components based upon the priors learnt from a training set of natural images. The JPEG compression process is simulated by a degradation model, represented by the signal attenuation and the Gaussian noise addition process. Based on the degradation model, the input image is locally filtered to remove Gaussian noise. Subsequently, the learning-based restoration algorithm reproduces the HF component to handle the attenuation process. Specifically, a Markov-chain based mapping strategy is employed to generate the HF primitives based on the learnt codebook. Finally, a quantization constraint algorithm regularizes the reconstructed image coefficients within a reasonable range, to prevent possible over-smoothing and thus ameliorate the image quality. Experimental results have demonstrated that the proposed scheme can reproduce higher quality images in terms of both objective and subjective quality.
Article Details
Unique Paper ID: 161299

Publication Volume & Issue: Volume 10, Issue 3

Page(s): 227 - 232
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