When a bound document such as a book is scanned or copied with a flat-bed scanner, there are two kinds of defects in the scanned
image; the geometric and photometric distortion. The root cause of the defects is the imperfect contact between the book to be scanned
and the scanner glass plate. The long gap between the book center and the glass plate causes the optical path from the surface of the
book and the imaging unit(CCD/CIS) to be different from the optimal condition.
In this paper, we propose a method for restoring bound document scan images without any additional information or sensor. We
correct the bound document images based on the estimation of the boundary feature and background profile. Boundary Feature is
obtained after calculating and analyzing the Minimum Boundary Rectangle which encloses the whole foreground contents with
minimum size and the extracted feature is used for correcting geometric distortion; de-skew, warping, and page separation.
Background profile is estimated from the gradient map and it is utilized to correct photometric distortion; exposure problem.
Experimental results show effectiveness of our proposed method.
When scanning a document that is printed on both sides, the image on the reverse can show through with high luminance.
We propose an adaptive method of removing show-through artifacts based on histogram analysis. Instead of attempting
to measure the physical parameters of the paper and the scanning system, or making multiple scans, we analyze the color
distribution to remove unwanted artifacts, using an image of the front of the document alone. First, we accumulate
histogram information to find the lightness distribution of pixels in the scanned image. Using this data, we set thresholds
on both luminance and chrominance to determine candidate regions of show-through. Finally, we classify these regions
into foreground and background of the image on the front of the paper, and show-through from the back. The
background and show-through regions become candidates for erasure, and they are adaptively updated as the process
proceeds. This approach preserves the chrominance of the image on the front of the papers without introducing artifacts.
It does not make the whole image brighter, which is what happens when a fixed threshold is used to remove show-through.
In this paper we propose an effective approach for creating nice-looking photo images of scenes having high dynamic
range using a set of photos captured with exposure bracketing. Usually details of dark parts of the scene are preserved in
over-exposed shot, and details of brightly illuminated parts are visible in under-exposed photos. A proposed method
allows preservation of those details by first constructing gradient field, mapping it with special function and then
integrating it to restore lightness values using Poisson equation. Resulting image can be printed or displayed on
Sharpness is an important attribute that contributes to the overall impression of printed photo quality. Often it is
impossible to estimate sharpness prior to printing. Sometimes it is a complex task for a consumer to obtain accurate
sharpening results by editing a photo on a computer.
The novel method of adaptive sharpening aimed for photo printers is proposed. Our approach includes 3 key techniques:
sharpness level estimation, local tone mapping and boosting of local contrast. Non-reference automatic sharpness level
estimation is based on analysis of variations of edges histograms, where edges are produced by high-pass filters with
various kernel sizes, array of integrals of logarithm of edges histograms characterizes photo sharpness, machine learning
is applied to choose optimal parameters for given printing size and resolution. Local tone mapping with ordering is
applied to decrease edge transition slope length without noticeable artifacts and with some noise suppression. Unsharp
mask via bilateral filter is applied for boosting of local contrast. This stage does not produce strong halo artifact which is
typical for the traditional unsharp mask filter.
The quality of proposed approach is evaluated by surveying observer's opinions. According to obtained replies the
proposed method enhances the majority of photos.
The red eye artifacts are troublesome defect of amateur photos. Correction of red eyes during printing without user
intervention and making photos more pleasant for an observer are important tasks.
The novel efficient technique of automatic correction of red eyes aimed for photo printers is proposed. This algorithm is
independent from face orientation and capable to detect paired red eyes as well as single red eyes. The approach is based
on application of 3D tables with typicalness levels for red eyes and human skin tones and directional edge detection
filters for processing of redness image. Machine learning is applied for feature selection. For classification of red eye
regions a cascade of classifiers including Gentle AdaBoost committee from Classification and Regression Trees (CART)
is applied. Retouching stage includes desaturation, darkening and blending with initial image. Several versions of
approach implementation using trade-off between detection and correction quality, processing time, memory volume are
The numeric quality criterion of automatic red eye correction is proposed. This quality metric is constructed by applying
Analytic Hierarchy Process (AHP) for consumer opinions about correction outcomes. Proposed numeric metric helped to
choose algorithm parameters via optimization procedure.
Experimental results demonstrate high accuracy and efficiency of the proposed algorithm in comparison with existing
The simplest way of halftoning color images using error diffusion is to apply scalar error diffusion technique to each of color channels independently. When processed independently for C, M, Y and K channels, cyan and magenta dots are often printed at the same pixel location. Such overlaps between cyan and magenta would appear as color noise in highlight area. Thus, it is desirable to minimize dot-on-dot printing of cyan and magenta especially in highlight area. In
order to further improve image quality, combined dot distribution of cyan and magenta should be homogeneous. Also, dot distribution of individual color channel should be even. In this paper, tone dependent error diffusion kernels and serpentine direction of processing are employed for homogeneous dot distribution of individual color channel. A decision rule based on updated values of cyan and magenta is applied to achieve dot-off-dot printing. A channel
dependent threshold modulation is proposed to improve combined distribution of cyan and magenta. A criterion to measure homogeneity of dot distribution is also proposed.
The perceived quality of the halftoned image strongly depends on the spatial distribution of the binary dots. Various error diffusion algorithms have been proposed for realizing the homogeneous dot distribution in the highlight and shadow regions. However, they are computationally expensive and/or require large memory space. This paper presents a new threshold modulated error diffusion algorithm for the homogeneous dot distribution. The proposed method is applied exactly same as the Floyd-Steinberg's algorithm except the thresholding process. The threshold value is modulated based on the difference between the distance to the nearest minor pixel, `minor pixel distance', and the principal distance. To do so, calculation of the minor pixel distance is needed for every pixel. But, it is quite time consuming and requires large memory resources. In order to alleviate this problem, `the minor pixel offset array' that transforms the 2D history of minor pixels into the 1D codes is proposed. The proposed algorithm drastically reduces the computational load and memory spaces needed for calculation of the minor pixel distance.
Error diffusion technique has been one of the most popular digital image halftoning methods. The quality of binary image resulting from the error diffusion technique is affected by the following three key factors; the values of error diffusion kernel, the locations of neighboring pixels for error propagation, and the quantization scheme. Among these factors, this paper is focused on the estimation of the values of error diffusion kernel. In previous efforts to propose modification to the original Floyd-Steinberg's algorithm, the values of error diffusion kernel have been determined by the trial and error method or by utilizing optimization techniques such as the least mean square estimation and neural network methods. This paper presents a new estimation method for the values of error diffusion kernel based on the genetic algorithm. Compared to the conventional optimization techniques, the genetic algorithm based approach lifts restrictions on the complexity of the error criterion for optimization. In this paper, two types of the error criteria are defined to improve image quality. They represent a measure of the reproduction of average brightness and an extent of undesirable artifacts appeared on the binary image for specific gray levels. The values of error diffusion kernel are estimated by simultaneously minimizing the defined error criteria using genetic algorithm. In the experiments, three types of error diffusion kernel are examined. The experimental results indicate that the binary images obtained based on the estimated error diffusion kernel exhibit less artifacts.
Various modifications to the Floyd-Steinberg's error diffusion algorithm have been proposed to reduce the undesirable
artifacts or to enhance the edges on the error diffused image. Most of the existing error diffusion techniques utilize the error
diffusion kernel defmed on the causal image plane with respect to the raster scanning directions. In this paper, an error
diffusion kernel containing non-causal neighbors is proposed for edge enhancement. The error diffusion kernel for individual
gray level is first estimated by minimizing the error criterion defined for the input gray level ramp image and its output binary
image. The proposed error diffusion kernel is then calculated by taking an average of the estimated error diffusion kernels
representing mid-tone gray levels. Experiments are performed to examine the proposed error diffusion method. Experimental
results indicate that the binary images obtained by the proposed error diffusion kernel exhibit the enhanced edges compared to
those from the existing error diffusion techniques.