Mohamed Nooman Ahmed

Characterization of Electrophotographic Print Artifacts: Banding, Jitter, and Ghosting

Electrophotographic (EP) print banding, jitter, and ghosting artifacts are common sources of print quality degradation. Traditionally, the characterization of banding and jitter artifacts relies mainly on the assumption that the defect has either a horizontal or vertical orientation which permits the simple 1-D analysis of the defect profile. However, this assumption can easily be violated if a small amount of printer or scanner skew is introduced to the analyzed images. In some cases, the defect can inherently be neither vertical nor horizontal. In this case, unless the defect orientation has been accurately detected before analysis, the 1-D-based approaches could bias the estimation of the defect severity. In this paper, we present an approach to characterize the jitter and banding artifacts of unrestricted orientation using wavelet filtering and 2-D spectral analysis. We also present a new system for detecting and quantifying ghosting defects. It includes a design for a printed test pattern to emphasize the ghosting defect and facilitate further processing and analysis. Wavelet filtering and a template matching technique are used to detect the ghost location along and across the scanned test pattern. A new metric is developed to quantify ghosting based upon its contrast, shape, and location consistency. Our experimental results show that the proposed approaches provide objective measures that quantify EP defects with a rank ordering correlation coefficient of 0.8 to 0.98, as compared to the subjective assessment of print quality experts.

Adaptive image interpolation using a multilayer neural network

Image resizing is an important operation that is used extensively in document processing to magnify or reduce images. Standard approaches fit the original data with a continuous model and then resample this 2D function on a few sampling grid. These interpolation methods, however, apply an interpolation function indiscriminately to the whole image. The resulting document image suffers from objectionable moire patterns, edge blurring and aliasing. Therefore, image documents must often be segmented before other document processing techniques, such as filtering, resizing, and compression can be applied. In this paper, we present a new system to segment and label document images into text, halftone images, and background using feature extraction and unsupervised clustering. Once the segmentation is performed, a specific enhancement or interpolation kernel can be applied to each document component. In this paper, we demonstrate the power of our approach to segment document images into text, halftone, and background. The proposed filtering and interpolation method results in a noticeable improvement in the enhanced and resized image.

Document image segmentation using a two-stage neural network

In this paper, we present a new system to segment and label document images into text, halftone images, and background using feature extraction and unsupervised clustering. Each pixel is assigned a feature pattern consisting of a scaled family of differential geometrical invariant features and texture features extracted from the cooccurence matrix. The invariant feature pattern is then assigned to a specific region using a two-stage neural network system. The first stage is a self-organizing principal components analysis (SOPCA) network that is used to project the feature vector onto its leading principal axes found by using principal components analysis. Using the SOPCA algorithm, we can train the SOPCA network to project our feature vector orthogonally onto the subspace spanned by the eigenvectors belonging to the largest eigenvalues. By doing that we ensure that the vector is represented by a reduced number of effective features. The next step is to cluster the output of the SOPCA network into different regions. This is accomplished using a self-organizing feature-map (SOFM) network. In this paper, we demonstrate the power of the SOPCA-SOFM approach to segment document images into text, halftone, and background.

Descreening using segmentation-based adaptive filtering

Ordered halftone patterns in the original document interact with the periodic sampling of the scanner, producing objectionable moir´e patterns. These are exacerbated when the copy is reprinted with an ordered halftone pattern. A simple, small low-pass filter can be used to descreen the image and to correct the majority of moir´e artifacts. Unfortunately, low-pass filtering affects detail as well, blurring it even further. Adaptive nonlinear filtering based on image features such as the magnitude and the direction of image gradient can also be used. However, non careful tuning of such filters could either cause damage to small details while descreeing the halftone areas, or result in less descreening while sharpening small details. In this paper, we present a new segmentation-based descreening technique. Scanned images are segmented into text, images and halftone classes using a multiresolution classification of edge features. The segmentation results guide a nonlinear, adaptive filter to favor sharpening or blurring of image pixels belonging to different classes. Our experimental results show the ability of the non-linear, segmentation driven filter of successfully descreening halftone areas while sharpening small size text contents.

EP printer jitter characterization using 2D Gabor filter and spectral analysis

Electrophotographic (EP) printer banding and jitter defects are main sources of print quality degradation. Traditionally, the characterization of these defects relies mainly on the assumption that the defect has either horizontal or vertical orientation to permit the simple ID analysis of the defect profile. However, this assumption can easily be violated if a small amount of printer or scanner skew is introduced to the analyzed images. In some cases the defect can inherently be neither vertical nor horizontal. Unless the defect orientation has been accurately detected before analysis, the ID-based approaches could bias the estimation of the defect severity. In this paper, we present an approach to characterize the skewed jitter defects of unrestricted orientation using 2D-based analysis of Gabor pre-filtering and spectral analysis. Our experimental results show that the proposed approach provides an objective measure that quantifies the EP jitter defect, and correlates to the subjective assessment of print quality experts with a 0.96 correlation coefficient.

Segmentation and enhancement of digital copies using a new fuzzy clustering method

In this paper, we introduce a new system to segment and label document images into text, halftoned images, and background using a modified fuzzy c-means (FCM) algorithm. Each pixel is assigned a feature vector, extracted from edge information and gray level distribution. The feature pattern is then assigned to a specific region using the modified fuzzy c-means approach. In the process of minimizing the new objective function, the neighborhood effect acts as a regularizer and biases the solution towards piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by scanner noise.

Content-based document enhancement by fuzzy clustering with spatial constraints

In this paper, we present a new system to segment and label the contents of scanned documents as either text or image, using a modified fuzzy c-means (FCM) algorithm. Each pixel is assigned a feature pattern extracted from the gray level distribution and computed at different scales. The invariant feature pattern is then assigned to a specific region using fuzzy logic. Our algorithm is formulated by modifying the objective function of the standard FCM algorithm to allow the labeling of a pixel to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution towards piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by scanner noise.