Image Fusion Using Gradient-Guided LiDAR Point Cloud Upsampling

The patent segments the image processing task by separately processing edge pixels and non-edge pixels. Edge pixels are processed using gradient calculation while non-edge pixels use interpolation based on gradient values. This segmentation allows each region to be processed with an appropriate method, reducing overall computational complexity while maintaining depth image resolution.

The patent extracts only the essential gradient information from images rather than using full deep learning models. By calculating gradients only at edge pixels and using these gradients to guide interpolation for non-edge pixels, the method extracts the minimum necessary information to achieve depth enhancement without the heavy computational burden of deep learning.

The patent performs gradient calculation at edge pixels as a preliminary step before generating the depth map. By pre-calculating gradient values at edge locations, the method prepares essential information in advance that guides the subsequent interpolation process, reducing the overall calculation time compared to using deep learning methods that process the entire image sequentially.

The patent uses gradient values from edge pixels to create interpolated depth values for non-edge pixels through copying and extrapolation. Instead of using complex deep learning models to predict every pixel, the method copies gradient information from edge pixels and uses it to generate depth values for surrounding non-edge pixels, significantly reducing calculation time while maintaining accuracy.