Visual Communication Design Based on Sparsity-Enhanced Image Processing Models

Abstract

In the field of visual communication, image clarity and accuracy are the key to convey effective information. A new sparsity-enhanced image processing model is introduced to address the limitations of traditional image processing models in terms of image resolution and fidelity. This model combines a deep neural networks learning framework with a sparse convolutional neural networks enhancement module to complete image reinforcement processing, thereby achieving more accurate image reconstruction techniques. Dictionary learning is used to train models so that the sparse representation of low resolution and high-resolution images has the same dictionary coefficients. By comparing with the existing techniques Enhanced Super-Resolution Generative Adversarial Network, Wide Activation for Efficient and Accurate Image Super-Resolution, and Bicubic Interpolation, and the new model achieves an average peak signalto-noise ratio of 32.9334 dB, which significantly outperforms the comparison group, respectively, with improvements of 1.9252 dB, 6.6509 dB, and 9.7297 dB, respectively. In addition, the new model demonstrates advantages in structural similarity and learning to perceive image block similarity, implying that it not only enhances the objective quality of the image, but also improves the subjective visual effect of the image. The improved resolution and fidelity of the output image confirms the model's superior performance in processing details and textures. This advancement not only improves the accuracy and efficiency of image processing techniques, but also provides strong technical support for the creation and dissemination of high-quality visual content, which is particularly suitable for application scenarios requiring highprecision visual displays, such as satellite image analysis, remote sensing detection and medical imaging.