Global And Local Feature Networks for Image Type Conversion

The patent segments the image processing task into multiple parallel processing streams: a first processing network handles global image features while a second processing network handles local image features. This segmentation allows each network to specialize in specific aspects of image enhancement, achieving superior image quality improvement compared to conventional single-network approaches.

The patent introduces a new dimensional aspect to image processing by simultaneously processing both global and local features in parallel dimensions. The global processing network operates on overall image characteristics while the local processing network operates on detailed regional features, creating a multi-dimensional enhancement approach that significantly improves image quality beyond traditional methods.

The patent divides the high-resolution image reconstruction task into parallel global and local processing streams. The global processing network efficiently captures overall structural information at lower computational cost, while the local processing network focuses computational resources on detailed feature reconstruction. This segmentation enables faster processing compared to conventional single-network approaches that must process all details sequentially.

The patent applies partial action by having different processing networks focus on different aspects of image resolution enhancement. The global processing network handles coarse-level structural reconstruction while the local processing network handles fine-level detail reconstruction. This distributed approach reduces overall processing time compared to attempting to process all resolution aspects in a single comprehensive pass.

The patent segments the feature extraction and processing task into two parallel pathways: a global processing network that captures overall image structure and context, and a local processing network that preserves and enhances local detailed features. By processing these segmented aspects separately and then combining results, the system achieves both global structural accuracy and local detail preservation, eliminating the trade-off present in conventional approaches.

The patent merges the outputs of the global processing network and local processing network to produce the final enhanced image. The global network provides overall structural context while the local network provides detailed feature information. By combining these merged results, the system achieves comprehensive image enhancement that retains both global structure and local details, resolving the contradiction between capturing overall shape and preserving local information.

The patent segments the processing task so that local feature processing occurs in a dedicated local processing network that preserves fine details, while simultaneously a global processing network captures overall contextual information. This segmentation ensures that local detail quality is enhanced without sacrificing global context, as both aspects are processed independently and then integrated.

The patent merges the locally-processed detailed features with globally-processed contextual information to produce the final enhanced image. The local processing network output provides high-quality local details while the global processing network output provides contextual framework. By merging these complementary results, the system achieves both local detail preservation and global context retention, eliminating the information loss that would occur with local-only processing.