An algorithm is presented for the detection of textured areas in natural images. Texture detection has potential application to image enhancement, tone correction, defect detection, content classification, and image segmentation. For example, texture detection may be useful for object detection when combined with color models and other descriptors. Sky, e.g., is generally smooth, and foliage is textured. The texture detector presented here is based on the intuition that texture in a natural image is comprised of many components. The measure we develop examines the structure of local regions of the image. This structural approach enables us to detect both structured and unstructured textures at many scales. Furthermore, it distinguishes between edges and texture, and also between texture and noise. Automatic detection results are shown to match human classification of corresponding image areas.
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.
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. This paper describes an algorithmic
solution to the dust and scratch removal task. The solution is divided into two phases: a detection phase
and a reconstruction phase. Some scanners have dedicated hardware to detect dust and scratch areas in the
original. Without hardware assistance, dust and scratch removal algorithms generally resort to blurring, at
the loss of image detail. We present an algorithmic alternative for dust and scratch detection that effectively
differentiates between defects and image details. In addition we present reconstruction algorithms, that preserve
image sharpness better than available alternatives. For detection we generate a detail-less image in which the
defects are "erased". We compare properties of the luminance channel of the input image relative to the detailless
image. For reconstruction of the defective areas we suggest both a fast small support algorithm and a large
support algorithm, which is better able to mimic the existing image texture.