Probabilistic RANSAC with Laplacian-Softmax Weighting for Sparse Image Reconstruction

This paper introduces a probabilistic variant of random sample consensus (RANSAC) for image reconstruction. Classical RANSAC samples pixel subsets uniformly to fit local models and identify inliers, but uniform draws are expensive on large images and fragile under widespread corruption. We replace them with likelihood-guided sampling that preserves the standard RANSAC pipeline while concentrating trials on cleaner pixels. Experiments on noisy images show comparable reconstruction quality with fewer iterations and substantially lower runtime.