噪声斑的模板匹配：对比度不变的GLR检验
Template matching with noisy patches: A contrast-invariant GLR test
摘要：
将来自噪声图像的块匹配到块字典中的原子是图像处理和计算机视觉中的许多技术的关键组成部分。通过用单个原子表示直到辐射变换都相同的所有补丁，字典大小可以保持较小，从而保持良好的计算效率。识别的原子在最佳匹配一个给定的噪声补丁，然后需要一个对比度不变的标准。根据检测理论，我们提出了一个新的标准，确保对比度不变性和噪声的鲁棒性。我们讨论了它的理论基础，并评估其性能下的高斯，伽玛和泊松噪声。
英文摘要：
Matching patches from a noisy image to atoms in a dictionary of patches is a key ingredient to many techniques in image processing and computer vision. By representing with a single atom all patches that are identical up to a radiometric transformation, dictionary size can be kept small, thereby retaining good computational efficiency. Identification of the atom in best match with a given noisy patch then requires a contrast-invariant criterion. In the light of detection theory, we propose a new criterion that ensures contrast invariance and robustness to noise. We discuss its theoretical grounding and assess its performance under Gaussian, gamma and Poisson noises.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
