Fast Intrinsic Images Multi-Scale Solver

The patent divides the image processing task into multiple scales (coarse to fine) and segments the computational workload across different resolution levels. The multi-scale approach processes the image at progressively finer resolutions, with each scale contributing to the final decomposed result. This segmentation of the processing task across scales enables parallel computation and reduces the computational burden at any single resolution level, thereby decreasing overall processing time while maintaining decomposition accuracy.

The patent employs dynamic iterative refinement across multiple scales, where the solution at coarser scales informs and accelerates the solution at finer scales. The iterative solver adapts its computation based on the current scale and convergence criteria, dynamically allocating computational resources. This dynamic approach allows the algorithm to spend more computational effort where needed (at finer scales for detail accuracy) while leveraging coarser scale results to reduce overall computation time.

The patent segments the computational problem into manageable scale-specific subproblems. By processing the image at multiple resolutions rather than attempting to solve the full-resolution problem directly, the computational complexity at each step is reduced. The multi-scale decomposition allows the algorithm to handle local variations and global structures separately, simplifying the overall computational task while preserving decomposition accuracy.

The patent introduces the scale dimension to the problem, transforming a single-resolution complex computation into a multi-resolution sequence of simpler computations. By adding this dimensional aspect, the algorithm can exploit the self-similarity and hierarchical structure of natural images, reducing computational complexity through the pyramid structure where coarse scales capture global information and fine scales capture local details.

The patent segments the image into a multi-scale pyramid structure, where each level represents a different resolution. This segmentation allows the computational workload to scale linearly with image resolution rather than quadratically or exponentially. By processing each scale independently and combining results, the method achieves O(n) scaling where n is the number of pixels, making it efficient for high-resolution images while maintaining decomposition accuracy through the hierarchical processing approach.

The patent implements dynamic adaptive processing where the computational effort at each scale is adjusted based on convergence criteria and image characteristics. The iterative solver dynamically terminates processing at coarser scales when sufficient information is obtained, preventing unnecessary computation. This dynamic adaptation ensures that the method scales efficiently with resolution by intelligently allocating computational resources across the scale pyramid rather than uniformly processing all resolutions.