Taylor Sun Flower Optimization-Based Compressive Sensing for Image Compression and Recovery. (12th January 2022) - R, Sekar; G, Ravi
Abstract: The most prominent challenges in compressive sensing are seeking the domain where an image is represented sparsely and hence be faithfully recovered to obtain high-quality results. This paper introduces an approach for image compression and recovery. The proposed approach involves two phases: the initial step is the compression phase, and the second step is the recovery phase. Initially, the medical image is subjected to the compression module wherein the self-similarity and the 3-dimensional (3D) transform are adapted for compressing the image. Then, in the recovery phase, the compressive sensing recovery is performed based on structural similarity index measure (SSIM)-based collaborative sparsity measure (S-CoSM), and the novel optimization algorithm, named Taylor-based Sunflower optimization (Taylor-SFO) algorithm. An effective S-CoSM measure is designed by modifying the CoSM using the SSIM metric. The proposed Taylor-SFO will be designed by integrating the Taylor series with the sunflower optimization (SFO) algorithm. The performance of the proposed Taylor-SFO approach is evaluated for matrices SSIM of 0.9412 and peak signal to noise ratio of 57.57 dB.