Recent research demonstrates the advantage of designing electro-optical imaging systems by jointly optimizing the optical
and digital subsystems. The optical systems designed using this joint approach intentionally introduce large and often
space-varying optical aberrations that produce blurry optical images. Digital sharpening restores reduced contrast due to
these intentional optical aberrations. Computational imaging systems designed in this fashion have several advantages
including extended depth-of-field, lower system costs, and improved low-light performance. Currently, most consumer
imaging systems lack the necessary computational resources to compensate for these optical systems with large aberrations
in the digital processor. Hence, the exploitation of the advantages of the jointly designed computational imaging system
requires low-complexity algorithms enabling space-varying sharpening.
In this paper, we describe a low-cost algorithmic framework and associated hardware enabling the space-varying finite
impulse response (FIR) sharpening required to restore largely aberrated optical images. Our framework leverages the
space-varying properties of optical images formed using rotationally-symmetric optical lens elements. First, we describe
an approach to leverage the rotational symmetry of the point spread function (PSF) about the optical axis allowing computational
savings. Second, we employ a specially designed bank of sharpening filters tuned to the specific radial variation
common to optical aberrations. We evaluate the computational efficiency and image quality achieved by using this low-cost
space-varying FIR filter architecture.
Recent research in the area of electro-optical system design identified the benefits of spherical aberration for
extending the depth-of-field of electro-optical imaging systems. In such imaging systems, spherical aberration
is deliberately introduced by the optical system lowering system modulation transfer function (MTF) and then
subsequently corrected using digital processing. Previous research, however, requires complex digital postprocessing
algorithms severely limiting its applicability to only expensive systems. In this paper, we examine the
ability of low-cost spatially invariant finite impulse response (FIR) digital filters to restore system MTF degraded
by spherical aberration. We introduce an analytical model for choosing the minimum, and hence cheapest, FIR
filter size capable of providing the critical level sharpening to render artifact-free images. We identify a robust
quality criterion based on the post-processed MTF for developing this model. We demonstrate the reliability
of the estimated model by showing simulated spherical coded imaging results. We also evaluate the hardware
complexity of the FIR filters implemented for various spherical aberrations on a low-end Field-Programmable
Gate Array (FPGA) platform.
Recently, joint analysis and optimization of both the optical sub-system and the algorithmic capabilities of
digital processing have created new digital-optical imaging systems with system-level benefits. We explore
a special class of digital-optical imaging systems called spherical coding that combine lens systems having
controlled amounts of spherical aberration with digital sharpening filters to achieve fast, low-cost, extended
depth-of-field (EDoF) imaging systems. We provide analysis of the optimal amount of spherical aberration
required as a function of desired depth-of-field extension. We also characterize the MSE-optimal filters
required to restore contrast. Finally, we describe a simple method to designing spherical coded systems and
demonstrate several advantages such as improved manufacturing yield using an actual lens design.
This paper reports on novel and traditional pixel and semantic operations using a recently standardized document representation called JPM. The JPM representation uses compressed pixel arrays for all visible elements on a page. Separate data containers called boxes provide the layout and additional semantic information. JPM and related image-based document representation standards were designed to obtain the most rate efficient document compression. The authors, however, use this representation directly for operations other than compression typically performed either on pixel arrays or semantic forms. This paper describes the image representation used in the JPM standard and presents techniques to (1) perform traditional raster-based document analysis on the compressed data, (2) transmit semantically meaningful portions of compressed data between devices, (3) create multiple views from one compressed data stream, and (4) edit high resolution document images with only low resolution proxy images.
Document imaging and transmission systems (typically MFPs) require both effective and efficient image rendering methods that support standard data formats for a variety of document types,
and allow for real time implementation. Since most conventional raster formats (e. g. TIFF, PDF, JPEG) are designed for use with either black and white text, or continuous-tone images, more specialized rendering methods are often required for representing mixed content documents. The baseline TIFF format supports a few binary compression options: PackBits, CCITT G3 and G4.
Conventionally, halftoning algorithms, such as error diffusion, can be used to create a binary representation of a document image in the TIFF format. However, PackBits, CCITT G3 and G4 compression generally do not produce desired compression on halftone images. In this paper, we propose an efficient error diffusion algorithm optimized for PackBits compression. This method, which we refer to as POED (PackBits optimized error diffusion), is a form of threshold
modulation error diffusion which takes advantage of the byte-oriented run length structure of PackBits compression by encouraging repetition of bytes in the resulting binary image. To maintain the sharpness of text, a binary segmentation algorithm is provided to switch off the adaptive error diffusion procedure and switch on the Floyd Steinberg error diffusion procedure in text regions. The POED method with PackBits compression yields higher
compression ratios than the conventional error diffusion method, while maintaining desirable visual quality with low computational and memory requirements. We show experimental results to compare our method with the Floyd Steinberg error diffusion method.
Conventional halftoning methods such as error diffusion and ordered dithering are poorly suited to the compression of halftone images using the baseline fax compression schemes CCITT G3 and G4. This paper proposes an efficient and flexible solution for binary representation of mixed content documents using CCITT G3/G4 compression. The solution includes two variations which we refer to as FastFax and ReadableFax. FastFax performs edge detection and text detection by applying locally adaptive binary thresholding and combines the two detection results together. The FastFax algorithm produces an accurate representation of binary mixed document content with high compressibility using CCITT G3/G4 compression. ReadableFax is based on FastFax and applies clustered dot screening to background and halftone regions to enhance graphic content. Both methods provide accurate representation of image content while allowing for substantial compressibility, and provide a tradeoff between representation quality and bit rate.
Effective document compression algorithms require scanned document images be first segmented into regions such as text, pictures and background. In this paper, we present a document compression algorithm that is based on the 3-layer (foreground/mask/background)MRC (mixture raster content) model. This compression algorithm first segments a scanned document image into different classes. Then, each class is transformed to the 3-layer MRC model differently according to the property of that class. Finally, the foreground and the back-ground layers are compressed using JPEG with customized quantization tables. The mask layer is compressed using JBIG2. The segmentation is optimized in the sense of rate distortion for the 3-layer MRC representation. It works in a closed loop fashion by a lying each transformation to each region of the document and then selecting the method that yields the best rate-distortion trade-off. The proposed segmentation algorithm can not only achieve a better rate-distortion trade-off, but also produce more robust segmentations by eliminating those mis-classifications which can cause severe artifacts. At similar bit rates, our MRC compression with the rate- distortion based segmentation can achieve a much higher subjective quality than state-of-the-art compression algorithms, such as JPEG and JPEG-2000.
We have successfully grown InGaAs detectors on the silicon substrate using the special technique of selective epitaxy. Small diameter (50 micrometers ) selective area depositions of In0.5Ga0.5As on silicon have exhibited a lower dislocation density, and hence, better electrical performance. These InGaAs detectors are grown by Molecular Beam Epitaxy (MBE). The final goal is to monolithically integrate InGaAs detectors with a silicon CMOS switched capacitor integrator. We have designed a CMOS switched-capacitor integrator (SCI) to realize a linear current-to-voltage conversion over a wide voltage range (-5 to +5 V) with low noise characteristics. The SCI circuit consists of an operational amplifier with a feedback capacitor and a reset switch. The SCI circuit uses +/- 5 V dual power supply and one -5 to +5 V voltage pulse generator. The circuit was simulated using PSPICE and the chip layout was done with the Mentor Graphics.