Multiresponse imaging: Information and fidelity

Multiresponse imaging is a process that acquires images, each with a different optical response, and reassembles them into a single image with an improved resolution that can approach times the photodetector-array sampling lattice. Our goals are to optimize the performance of this process in terms of the resolution and fidelity of the restored image and to assess the amount of information required to do so. The theoretical approach is based on the extension of both image restoration and rate distortion theories from their traditional realm of signal processing to image processing which includes image gathering and display.

[1]  Roger Y. Tsai,et al.  Multiframe image restoration and registration , 1984 .

[2]  Dennis C. Ghiglia Space-invariant deblurring given N independently blurred images of a common object , 1984 .

[3]  Wen-Yu Su,et al.  Recursive high-resolution reconstruction of blurred multiframe images , 1993, IEEE Trans. Image Process..

[4]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[5]  Claude E. Shannon,et al.  The Mathematical Theory of Communication , 1950 .

[6]  Zia-ur Rahman,et al.  Image gathering and digital restoration for fidelity and visual quality , 1991, CVGIP Graph. Model. Image Process..

[7]  David Lindley,et al.  Information and Decision Processes. , 1962 .

[8]  Peter No,et al.  Digital Coding of Waveforms , 1986 .

[9]  B. R. Hunt,et al.  Digital Image Restoration , 1977 .

[10]  F. O. Huck,et al.  An information theory of image gathering , 1991, Inf. Sci..

[11]  Nikolas P. Galatsanos,et al.  Digital restoration of multichannel images , 1989, IEEE Trans. Acoust. Speech Signal Process..

[12]  Toby Berger,et al.  Rate distortion theory : a mathematical basis for data compression , 1971 .

[13]  Friedrich O. Huck,et al.  Image gathering and restoration: information and visual quality , 1989 .

[14]  John E. Dennis,et al.  Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.

[15]  F. O. Huck,et al.  Wiener restoration of sampled image data: end-to-end analysis , 1988 .

[16]  Nirmal K. Bose,et al.  Recursive reconstruction of high resolution image from noisy undersampled multiframes , 1990, IEEE Trans. Acoust. Speech Signal Process..

[17]  T. S. Huang,et al.  Advances in computer vision & image processing , 1988 .

[18]  Aggelos K. Katsaggelos A multiple input image restoration approach , 1990, J. Vis. Commun. Image Represent..

[19]  R. Gallager Information Theory and Reliable Communication , 1968 .

[20]  Thomas S. Huang,et al.  Picture Processing and Digital Filtering , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Stephen E. Reichenbach,et al.  Small convolution kernels for high-fidelity image restoration , 1991, IEEE Trans. Signal Process..

[22]  William F. Schreiber,et al.  Fundamentals of Electronic Imaging Systems , 1986 .