Ilia V. Safonov

Automatic red eye correction and its quality metric

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 possible. 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 solutions.

Adaptive sharpening of photos

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 observers opinions. According to obtained replies the proposed method enhances the majority of photos.

Automatic correction of exposure problems in photo printer

In this paper we consider a problem of automatic enhancement of amateur photos in photo printer. The purpose of correction consists of making photos more pleasant for an observer. The photos with various exposure problems and with poorly distinguishable details in shadow areas are analyzed. Our approach is based on contrast stretching and alpha-blending of both brightness of the initial image and estimations of reflectance. For obtaining reflectance estimation a simplified illumination model is used. The luminance is estimated using bilateral filter. Reflectance is estimated using heuristic functions of ratio between brightness of the initial image and estimation of luminance. The correction parameters are chosen adaptively based on histogram analysis. Noise suppression and some sharpening occur during correction. The time and memory optimization issues are considered. Look-up tables and recursive separable bilateral filter are applied to speed up the algorithm. The quality of the algorithm is evaluated by surveying of observers opinions and by comparisons with already existing software and hardware solutions for local shadow correction. The proposed algorithm was implemented into firmware of Samsung dye-sublimation compact photo printer

Content-based image orientation recognition

In this chapter, we describe a method for digital image orientation recognition. The method is based on classifier learning by a set of feature vectors extracted from images. Feature vectors are flip-invariant to effectively classify images into portrait-oriented and landscape-oriented photos. A new texture feature is proposed based on the observation that more textured areas are usually located in the lower part of the image. The proposed method could be effectively applied to index prints of photos (printing a set of miniatures of a large image collection).

Generation of PDF with vector symbols from scanned document

The paper is devoted to the algorithm for generation of PDF with vector symbols from scanned documents. The complex multi-stage technique includes segmentation of the document to text/drawing areas and background, conversion of symbols to lines and Bezier curves, storing compressed background and foreground. In the paper we concentrate on symbol conversion that comprises segmentation of symbol bodies with resolution enhancement, contour tracing and approximation. Presented method outperforms competitive solutions and secures the best compression rate/quality ratio. Scaling of initial document to other sizes as well as several printing/scanning-to-PDF iterations expose advantages of proposed way for handling with document images. Numerical vectorization quality metric was elaborated. The outcomes of OCR software and user opinion survey confirm high quality of proposed method.

High-performance automatic cropping and deskew of multiple objects on scanned images

The paper is devoted to a novel high-performance algorithm for automatic segmentation and skew correction of several objects on a scanned image. The complex multi-stage technique includes preprocessing, initial segmentation, classification of connected regions, merging of fragmented regions by heuristic procedure, bounding boxes detection and deskew of rectangular objects. Our method is highly effective owing to unification most of operations in one pass. Algorithm provides users with additional functionality and comfort. The method is evaluated by suggested quantitative quality criteria.

Descreening of scanned images

Screen or halftone pattern appears on the majority of images printed on electrophotographic and ink-jet printers as well as offset machines. When such halftoned image is scanned, a noisy effect called a Moiré pattern often appears on the image. There are plenty of methods proposed for descreening of images. Common way is adaptive smoothing of scanned images. However the descreening techniques face the following dilemma: deep screen reduction and restoration of contone images leads to blurring of sharp edges of text and other graphics primitives, on the other hand insufficient smoothing keeps screen in halftoned areas. We propose novel descreening algorithm that is primarily intended for preservation of sharpness and contrast of text edges and for restoration contone images from halftone ones accurately. Proposed technique for descreening of scanned images comprises five steps. The first step is decrease of edge transition slope length via local tone mapping with ordering; it is carried out before adaptive smoothing, and it allows better preservation of edges. Adaptive low-pass filter applies simplified idea of Non-Local Means filter for area classification; similarity is calculated between central block of window and different adjacent block that is selected randomly. If similarity is high then current pixel relates to flat region, otherwise pixel relates to edge region. For prevention of edges blurring, flat regions are smoothed stronger than edge regions. By random selection of blocks we avoid the computational overhead related to excessive directional edge detection. Final three stages include additional decrease of edge transition slope length using local tone mapping, increase of local contrast via modified unsharp mask filter, that uses bilateral filter with special edge-stop function for modest smoothing of edges, and global contrast stretching. These stages are intended to compensate decreasing of sharpness and contrast due to low-pass filtering, it allows to enhance visual quality of scanned image. For parameters adjusting for different scanning resolutions and comparison with existing techniques test target and criteria were proposed. Also the quality of proposed approach is evaluated by surveying observers opinions. According to obtained outcomes the proposed algorithm demonstrates good descreening capabilities.

Automatic Generation of Collage

We describe algorithms for the automatic creation of collages from a collection of photos. The appeal of different forms of collage from the user’s viewpoint is discussed. We consider time- and camera-based photo selection procedures, including the estimation of the quality of the photographs. Collage generation involves arrangement of the photos on canvas and an application of seamless blending with elements of randomness.

Automatic Red Eye Correction

The red eye artefact is an irritating defect in photos. The correction of red eyes during printing without user intervention is an important task. This chapter is devoted to a description of an efficient technique for automatic correction of red eyes. Initially we developed a method for a photo printer; however, our approach is applicable for any software and firmware. The algorithm is independent of face orientation and is capable of detecting paired red eyes as well as single red eyes. For the segmentation of roundish red regions, we applied colour information and thresholding in the domain of outcomes of four-directional edge-detection filters jointly. For classification of segmented regions, we built a cascade of classifiers: three simple classifiers eliminate obviously false areas, and after that an ensemble of decision trees created by an adaptive boosting algorithm performs detection of red-eye regions with good performance. A retouching stage includes desaturation, darkening, and blending with the initial image. In addition, we construct a sophisticated quality criterion of correction: we employ the Analytic Hierarchy Process for prioritization of the observer’s opinions about outcomes of detection and correction. The experimental results demonstrate good performance of the proposed algorithm in comparison with existing solutions.

Removal of JPEG Artefacts

The present chapter generally relates to a method for effective reduction of artefacts caused by lossy compression algorithms based on block-based Discreet Cosine Transform (DCT) coding, known as JPEG coding. The most common artefacts produced by this type of coding are blocking and ringing artefacts. To reduce the effect of coding artefacts caused by significant information loss, a variety of different algorithms and methods have been suggested. However, the majority of solutions propose to process all blocks in the image, even those blocks that are not affected by artefacts and this leads to an increase in processing time and required resources, as well as image over-blurring. Techniques for ringing artefact detection usually rely on an edge-detection step, a complicated and versatile procedure with unknown optimal parameters. In this paper, we describe very effective procedures for the detection of artefacts and their subsequent correction. This approach helps to save a notable amount of computational resources, since not all the blocks are involved in correction procedures. Detection steps are performed in the frequency domain, using only the DCT coefficients of an image. Numerous examples have been analysed and compared with the existent solutions, and the results prove the effectiveness of the proposed technique.