Sparse Representation and Adaptive Filtering Techniques for Real-Time Image and Speech Signal Enhancement

Abstract

Enhancing of real time images and speech signals are the basic needs of the current multimedia,
surveillance, communication and human-machine interaction systems where the quality of
the signal should be enhanced with hard and fast latency and computation restrictions.
Traditional filtering methods such as linear and adaptive filters usually do not have high
ability of preserving fine structural-based information and perceptual attributes especially
in non-stationary and high-noise settings. This paper attempts to solve the limitations and
presents unification of the sparse representation-based adaptive enhancements framework
on real-time image and speech signals. The offered methodology manipulates the natural
signal thinness by patch-wise sparse coding of natural signals using the Orthogonal Matching
Pursuit algorithm and learning dictionaries with the K-SVD algorithm accompanied with
adaptive filtering mechanisms to dynamically adjust to changing noise properties over time.
An adaptive filtering approach that is sparse-based is adopted to provide a high level of
robustness and convergence capability in a non-stationary scenario. Rather a widespread
of experiments carried out with standard benchmark datasets proves that the presented
framework performs much better than traditional filtering and available sparse-based
methods. Quantitative analysis based on image enhancement measures such as Peak Signal
to-Noise Ratio and Structural Similarity Index gives results that indicate that there are
substantial enhancements in noise reduction and structural preservation. In addition, real
time feasibility is confirmed by computing complexity and latency analysis which show that
the proposed approach is capable of providing high-quality enhancement meeting real-time
processing conditions. These findings demonstrate the usefulness of sparse representation
as complemented by adaptive filtering as the sole means of signal enhancement in a robust
and efficient manner in real-time.