A method for contrast enhancement is proposed. The algorithm is based on a local and image-dependent exponential correction. The technique aims to correct images that simultaneously present overexposed and underexposed regions. To prevent halo artifacts, the bilateral filter is used as the mask of the exponential correction. Depending on the characteristics of the image (piloted by histogram analysis), an automated parameter-tuning step is introduced, followed by stretching, clipping, and saturation preserving treatments. Comparisons with other contrast enhancement techniques are presented. The Mean Opinion Score (MOS) experiment on grayscale images gives the greatest preference score for our algorithm.
Despite the great advances that have been made in the field of digital photography and CMOS/CCD sensors, several
sources of distortion continue to be responsible for image quality degradation. Among them, a great role is played by
sensor noise and motion blur. Of course, longer exposure times usually lead to better image quality, but the change in the
photocurrent over time, due to motion, can lead to motion blur effects. The proposed low-cost technique deals with the
aforementioned problem using a multi-capture denoising algorithm, obtaining a good quality with sensible reduction of
the motion blur effects.
DCT based compression engines1,2 are well known to introduce color artifacts on the processed input frames, in
particular for low bit rates. In video standards, like MPEG-23, MPEG-44, H2635, and in still picture standards, like
JPEG6,7, blocking and ringing distortions are understood and considered, so different approaches have been developed to
reduce these effects8,9,10,11. On the other side, other kinds of phenomenon have not been deeply investigated. Among
them, the chromatic color bleeding effects has only recently received proper attention12,13. The scope of this paper is to
propose and describe an innovative and powerful algorithm to overcome this kind of color artifacts.
An automatic natural scenes classifier and enhancer is presented. It works mainly by combining chromatic and positional criterions in order to classify and enhance portraits and landscapes natural scenes images. Various image processing applications can easily take advantage from the proposed solution, e.g. automatically drive camera settings for the optimization of exposure, focus, or shutter speed parameters, or post processing applications for color rendition optimization. A large database of high quality images has been used to design and tune the algorithm, according to wide accepted assumptions that few chromatic classes on natural images have the most perceptive impact on the human visual system. These are essentially skin, vegetation and sky?sea. The adaptive color rendition technique, which has been derived from the results produced by the image classifier, is based on a simple yet effective principle: it shifts the chromaticity of the regions of interest towards the statistically expected ones. Introduction of disturbing color artifacts is avoided by a proper modulation and by preservation of original image luminance values. Quantitative results obtained over an extended data set not belonging to the training database, show the effectiveness of the solution proposed both for the natural image classification and the color enhancement techniques.
This paper describes an automatic technique able to fuse different images of the same scene, acquired with different camera settings, in order to obtain an enhanced single representation of the interested. This allows to extend the functionalities (depth of field, dynamic range) of medium and low cost digital cameras. When Multi-Scale Decomposition (MSD) is used on differently focused images, magnification and blurring effects of lens focusing systems often compromise the final image with unpleasant artifacts. In our approach new techniques able to reduce these artifacts are introduced. Even if the algorithm has been essentially designed to extend depth of field it can be also used on multi-exposed input images thus extending dynamic range. The algorithm can be applied on full colorand on Color Filter Array (CFA)images.
The paper presents a collection of methods and algorithms able to deal with high dynamic range of real pictures acquired by digital engines (e.g. CCD/CMOS cameras). Accurate image acquisition can be not well suited under difficult light conditions. A few techniques that overcome the usual 8 bit-depth representations by using differently exposed pictures and recovering the original radiance values are reported. This allows capturing both low and highlight details, fusing the various pictures into a singe map, thus providing a more faithful description of what the real world scene was. However in order to be viewed on a common computer monitor the map needs to be re-quantized while preserving visibility of details. The main problem comes from the fact that usually the contrast of the radiance values is far greater than that of the display device. Various related techniques are reviewed and discussed.