A novel automatic video deshearing is proposed to reduce the skew artifacts (jelly artifacts) in sequences captured by CMOS censor cameras. The paper first considers the principles of skew artifacts in rolling shutter cameras. Then a deshearing algorithm with high accuracy is discussed with automatic skew artifact detection. A video completion step is also implemented to reconstruct unavailable pixels in the new grid of the desheared frame. Simulation results show that the proposed method effectively reduces the skew artifacts for different sequences captured by rolling shutter cameras.

As an effort to improve the video quality of a digital TV (HDTV) system, a robust motion adaptive deinterlacing algorithms based on motion decision feedback rules are proposed, which, in principle, utilizes the motion decision information of the past and the current pictures. With the proposed algorithm, visual artifacts due to the failure in detecting a fast repetitive motion embedded in an interlaced video sequence can be significantly reduced. Hence, with the proposed algorithms, much more pleasing video quality can be realized for the HDTV systems than typical motion adaptive deinterlacing algorithms.

Histogram equalization is a widely used scheme for contrast enhancement in a variety of applications due to its simple function and effectiveness. One possible drawback of the histogram equalization is that it can change the mean brightness of an image significantly as a consequence of histogram flattening. Clearly, this is not a desirable property when preserving the original mean brightness of a given image is necessary. As an effort to overcome such drawback for extending the applications of the histogram equalization in consumer electronic products, bi-histogram equalization has been proposed by the author which is capable of preserving the mean brightness of an image while it performs contrast enhancement. The essence of the bi-histogram equalization is to utilize independent histogram equalizations separately over two subimages obtained by decomposing the input image based on its mean. A simplified version of the bi-histogram equalization is proposed, which is referred to as the quantized bi-histogram equalization. The proposed algorithm provides a much simpler hardware (H/W) structure than the bi-histogram equalization since it is based on the cumulative density function of a quantized image. Thus, the realization of bi-histogram equalization in H/W is feasible, which leads to versatile applications in the field of consumer electronics.

The Ll filters constitute a wide class of filters including both linear and L filters, and it has been shown that they outperform linear and L filters. A close examination of the optimization of the Ll filters in real applications, however, casts a problem of importance that the Ll filters can be degraded from insufficient signal statistics for optimization since a sufficiently long training data sequence is not tractable in practice for the Ll filters, especially, when the the number of input taps is large. We formally propose a new class of order-statistic filters referred to as the decolored Ll filter lattice, which exploits a decolored rank permutation space. By decoloring the rank permutation space, having sufficient training data for optimization in practice is feasible. Various filters are obtained in the class of decolored Ll filters for different orders of decoloring. It is shown that the set of decolored Ll filters of all orders form a lattice structure. At the simplest level in the structure, the decolored Ll filters reduce to a simple linear (FIR) filter, and we obtain the Ll filter at the highest level in the structure.

The problem of reconstructing a continuous-tone image given its ordered dithered halftone or its error-diffused halftone image is considered. We develop a modular class of nonlinear filters that can reconstruct the continuous-tone information preserving image details and edges that provide important visual cues. The proposed nonlinear reconstruction algorithms, denoted as binary permutation filters, are based on the space and rank orderings of the halftone samples provided by the multiset permutation of the "on" pixels in a halftone observation window. For a given window size, we obtain a wide range of filters by varying the amount of space-rank ordering information utilized in the estimate. For image reconstructions from ordered dithered halftones, we develop periodically space-varying filters that can account for the periodical nature of the underlying screening process. A class of suboptimal but simpler space-invariant reconstruction filters are also proposed and tested. Constrained LMS type algorithms are employed for the design of reconstruction filters that minimize the reconstruction mean squared error. We present simulations showing that binary permutation filters are modular, robust to image source characteristics, and that they produce high visual quality image reconstruction.

The problem of reconstructing a continuous-tone image given its ordered dithered halftone image is considered. We utilize a modular class of non-linear filters, denoted as binary permutation filters, which can reconstruct the continuous-tone information as well as image details which provide important visual cues. Binary permutation filters are based on the space-rank ordering of the halftone samples which is provided by the multiset permutation of the "on" pixels in a halftone observation window. By varying the space-rank order information utilized in the estimate, for a given window size, we obtain a wide range of filters. We present simulations showing that binary permutation filters are modular, robust to image source characteristics, and that their results produce high visual quality image reconstruction.>