Smooth Gyrated Texel Quadrivium Network for 3D Retinal OCT Image Compression

Image denoising is a crucial pre-processing technique in retinal optical coherence tomography image compression, but existing methods struggle with signal-dependent noise and do not consider hybridized low-contrast residual noise (HLRN), failing to gather information from images. Thus, the novel Smooth Gyrated Texel Quadrivium Network (SGTQN) is proposed to reduce noise and collect self-sufficient information. In the SGTQN, the novel Additive Ascombe Smooth Sifter converts Poisson noise into Gaussian noise using the Ascombe Transform and removes unwanted Gaussian noise and the HLRN by hybridized noise removal, thus effectively gathering useful information from the image. After denoising, existing segmentation methodologies neglect the retinal nerve deviation value, creating a poor self-explanatory image. Thus, a novel Improvised Gyrated Alexa Net incorporates the Standardized Gyrated Layer, which considers the deviation values, thus generating a self-explanatory segmented image. Furthermore, many existing compression methods failed to achieve a higher quality image due to their non-uniform compression. The Texel Quadrivium Convolutional Network modifies the pooling layer into a Texel Quadrivium Layer to perform uniform compression and uses adjuvant vector coordinates to generate a high-resolution compressed image. This proposed model provides high-quality image compression with reduced noise, with a high accuracy of 95% and a lower mean square error of 0.02.