Stephen K. Park

Image reconstruction by parametric cubic convolution

Abstract A parametric implementation of cubic convolution image reconstruction is presented which is generally superior to the standard algorithm and which can be optimized to the frequency content of the image.

Modulation-transfer-function analysis for sampled image systems

Sampling generally causes the response of a digital imaging system to be locally shift-variant and not directly amenable to MTF analysis. However, this paper demonstrates that a meaningful system response can be calculated by averaging over an ensemble of point-source system inputs to yield an MTF which accounts for the combined effects of image formation, sampling, and image reconstruction. As an illustration, the MTF of the Landsat MSS system is analyzed to reveal an average effective IFOV which is significantly larger than the commonly accepted value, particularly in the along-track direction where undersampling contributes markedly to an MTF reduction and resultant increase in image blur.

Image sampling, reconstruction, and the effect of sample-scene phasing

This paper is a 1-D analysis of the degradation caused by image sampling and interpolative reconstruction. The analysis includes the sample-scene phase as an explicit random parameter and provides a complete characterization of this image degradation as the sum of two terms: one term accounts for the mean effect of undersampling (aliasing) and nonideal reconstruction averaged over all sample-scene phases; the other term accounts for variations about this mean. The results of this paper have application to the design and performance analysis of image scanning, sampling, and reconstruction systems.

Aliasing and blurring in 2-D sampled imagery

The quality of image reconstructions from discrete data suffers not only from the blurring of spatial detail caused by limitations in the spatial frequency response of electrooptical systems, but also from the aliasing generated if spatial detail has been undersampled. P. Mertz and F. Grey [Bell Syst. Tech. J. 13, 464 (1934)] and O. H. Schade [J. Soc. Motion Pict. Telev. Eng. 56, 131 (1955); 58, 181 (1952); 61, 97 (1953); 64, 593 (1955)] have observed that reasonable spot intensity profiles and photosensor aperture shapes of equivalent size result in about equal blurring but that some profiles and shapes suppress aliasing better than others. This paper presents quantitative results of the magnitude of aliasing and blurring as a function of random radiancefields typical for natural scenes and of spatial responses and sampling intervals typical for TV cameras and optical-mechanical scanners. These results indicate that aliasing may often be a larger source of degradation than either blurring or electronic noise.

Estimation of spectral reflectance curves from multispectral image data

A technique is presented for estimating spectral reflectance curves from multispectral image data even if the spectral samples are obtained from channels whose spectral responsivity is not narrowband. It is demonstrated that these reflectance estimates can be written as a linear combination of the spectral samples and that, analogous to Shannons sampling theorem, if the spectral reflectance is a natural cubic spline, it can be estimated exactly provided the number of spectral channels is sufficiently large. Simulation results suggest that the accuracy of the spectral reflectance estimates is quite good and very insensitive to the spectral responsivity shapes.

Topics in the two-dimensional sampling and reconstruction of images

Abstract The arrival of the new generation of satellite sensors with improved spatial and radiometric resolution (Thematic Mapper and SPOT) requires a re-evaluation of techniques for system performance analysis. Furthermore, the high performance of these systems will place greater demands on resampling algorithms for geometric processing of the imagery. Both of these topics are discussed. A formalism for incorporating random sample-scene phase in system analysis is presented and an improved bicubic resampling function is proposed and compared to standard resampling functions with tests on real imagery.

Information efficiency of line-scan imaging mechanisms

Information theory is used to formulate a single figure of merit for assessing the performance of line-scan imaging systems as a function of their spatial response (PSF or MTF), sensitivity, and sampling and quantization intervals and of the statistical properties of a random radiance field. Information density and efficiency (i.e., the ratio of information density to data density) tend to be optimum when the MTF and sampling passband of the imaging system are matched to the Wiener spectrum of the radiance field. Computational results for the statistical properties of natural radiance fields and the responses of common line-scan imaging mechanisms indicate that information density and efficiency are not strongly sensitive to variations in typical statistical properties of the radiance field and that the best practically realizable performance is approached when the sampling intervals are ~0.5-0.7 times the equivalent diameter of the PSF.

Optical-mechanical line-scan imaging process: its information capacity and efficiency

An expression for the information capacity of the optical-mechanical line-scan imaging process is derived, which includes the effects of blurring of spatial detail, photosensor noise, aliasing, and quantization. Both the information capacity for a fixed data density and the information efficiency (i.e., the ratio of information capacity to data density) exhibit a distinct single maximum when displayed as a function of sampling rate, and the location of this maximum is determined by the system frequency response shape, SNR, and quantization interval.

Image-plane processing of visual information.

Shannon’s theory of information is used to optimize the optical design of sensor-array imaging systems which use neighborhood image-plane signal processing, similar to the lateral inhibitory preprocessing in natural vision, for enhancing edges and compressing dynamic range during image formation. The resultant edge-enhancement, or bandpass-filter, response is found to be similar to that of (Marr’s model of) human vision. Comparisons of traits in natural vision with results from information theory suggest that image-plane processing can improve visual information acquisition for pattern recognition when resolving power, sensitivity, and dynamic range are constrained. The improvements that can be attained for constructing edge-enhanced images and primal sketches include reduced sensitivity to changes in light levels reduced data transmission, and reduced data processing.

Information Density And Efficiency Of Two-Dimensional (2-D) Sampled Imagery

Information density and efficiency (i.e., the ratio of information density to data density) are used as criteria for assessing the quality of 2-D sampled and quantized imagery as a function of the statistical properties of random radiance fields, the spatial response (PSF or MTF) and sensitivity of imaging systems, and the sampling and quantization intervals. Computational results are intuitively satisfying: they are consistent with experimental and theoretical results obtained by earlier investigators concerned with the performance of TV cameras, and they provide useful guidelines for optimizing the design of line-scan and sensor-array imaging systems, especially if these systems use a digital communication link for transmitting data.