It has been shown that electro-optical imaging systems designed by integrating optics and digital processing provide system-level advantages such as extended depth-of-field and lower optical component costs. In such imaging systems, the strength of the optical aberration or blur can dramatically change with the f-number, and hence different digital filters in the subsequent digital processing are required to correct the captured images at different f-numbers. However, implementing such number of filters in hardware requires expensive computational resources, which in turn increase the overall system cost. In this paper, we propose a simple image filtering approach which uses a weighted sum of a set of component finite impulse response (FIR) filters to effectively apply a different composite FIR filter for each f-number. The simulation results demonstrate that our approach achieves desirable image quality for variable f-numbers, while substantially reducing the complexity in hardware implementation.

The mixed raster content (MRC) model can be used to implement highly effective document compression algorithms. MRC document coders are typically based on the use of a binary mask layer that efficiently encodes the text and graphic content. However, while many MRC-based methods can yield much higher compression ratios than conventional color image compression methods, the binary representation tends to distort fine document details, such as thin lines and text edges. In this paper, we propose a method for encoding and decoding the binary mask layer that substantially improves the decoded document fidelity of text and graphics at a fixed bit rate. This method, which we call resolution-enhanced rendering (RER), works by adaptively dithering the encoded binary mask, and then applying a nonlinear predictor to decode a gray level mask at the same resolution. Both the dithering and nonlinear prediction algorithms are jointly optimized to produce the minimal distortion rendering. In addition, we introduce a second method, interpolative RER (IRER), which incorporates interpolation into the MRC decoder. The IRER method increases the compression ratio by allowing a high-resolution document to be coded at lower resolutions. We present experimental results illustrating the performance of our RER/IRER methods and comparing them to some existing MRC-based compression algorithms.

Raster document coders are typically based on the use of a binary mask layer that efficiently encodes the text and graphic content. While these methods can yield much higher compression ratios than natural image compression methods, the binary representation tends to distort fine document details, such as thin lines, and text edges. In this paper, we describe a method for encoding and decoding the binary mask layer that substantially improves the decoded document quality at a fixed bit rate. This method, which we call resolution enhanced rendering (RER), works by adaptively dithering the encoded binary mask, and then applying a nonlinear predictor to decode a gray level mask at the same or higher resolution. We present experimental results illustrating that the RER method can substantially improve document quality at high compression ratios.