Ron Maurer

Vanishing points estimation by self-similarity

This paper presents a novel self-similarity based approach for the problem of vanishing point estimation in man-made scenes. A vanishing point (VP) is the convergence point of a pencil (a concurrent line set), that is a perspective projection of a corresponding parallel line set in the scene. Unlike traditional VP detection that relies on extraction and grouping of individual straight lines, our approach detects entire pencils based on a property of 1D affine-similarity between parallel cross-sections of a pencil. Our approach is not limited to real pencils. Under some conditions (normally met in man-made scenes), our method can detect pencils made of virtual lines passing through similar image features, and hence can detect VPs from repeating patterns that do not contain straight edges. We demonstrate that detecting entire pencils rather than individual lines improves the detection robustness in that it improves VP detection in challenging conditions, such as very-low resolution or weak edges, and simultaneously reduces VP false-detection rate when only a small number of lines are detectable.

Comprehensive solutions for automatic removal of dust and scratches from images

Dust, scratches, or hair on originals (prints, slides, or negatives) distinctly appear as light or dark artifacts on a scan. These unsightly artifacts have become a major consumer concern. There are several scenarios for removal of dust and scratch artifacts. One scenario is during acquisition, e.g., while scanning photographic media. Another is artifact removal from a digital image in an image editor. For each scenario, a different solution is suitable, with different performance requirements and differing levels of user interaction. This work describes a comprehensive set of algorithms for automatically removing dust and scratches from images. Our algorithms solve a wide range of use scenarios. A dust and scratch removal solution has two steps: a detection step and a reconstruction step. Very good detection of dust and scratches is possible using side information, such as provided by dedicated hardware. Without hardware assistance, dust and scratch removal algorithms generally resort to blurring, thereby losing image detail. We present algorithmic alternatives for dust and scratch detection. In addition, we present reconstruction algorithms that preserve image detail better than previously available alternatives. These algorithms consistently produce visually pleasing images in extensive testing.

Denoising scheme for realistic digital photos from unknown sources

This paper targets denoising of digital photos taken by cameras with unknown sensor parameters and image processing pipeline. Common noise characteristics in such images originate from camera-internal processing, such as demosaicing, tone mapping, and JPEG compression. Three of the noise characteristics that are not adequately addressed by existing denoising algorithms are spatially correlated low-frequency noise, strong signal dependency of the noise level and high levels of the chrominance noise relative to the luminance noise. We propose a generic scheme that extends existing denoisers such as the bilateral filter to account for all the problems above. Our solution combines a novel progressive pyramidal filtering scheme to address the correlated noise, filter adaptation via local noise level estimation and luminance-guided chrominance filtering to address the low-SNR of the chrominance channels. We demonstrate the effectiveness of our solution for challenging realistic noisy photos.