Image enhancement with art design: a visual feature approach with a CNN-transformer fusion model.

ABSTRACT: Graphic design, as a product of the burgeoning new media era, has seen its users' requirements for images continuously evolve. However, external factors such as light and noise often cause graphic design images to become distorted during acquisition. To enhance the definition of these images, this paper introduces a novel image enhancement model based on visual features. Initially, a histogram equalization (HE) algorithm is applied to enhance the graphic design images. Subsequently, image feature extraction is performed using a dual-flow network comprising convolutional neural network (CNN) and Transformer architectures. The CNN employs a residual dense block (RDB) to embed spatial local structure information with varying receptive fields. An improved attention mechanism module, attention

SUBMITTER: Xu M 

PROVIDER: S-EPMC11623052 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature