Tài liệu 53 Image and Video Restoration pdf

A. Murat Tekalp. “Image and Video Restoration.”

2000 CRC Press LLC. <http://www.engnetbase.com>.

Image and Video Restoration

A. Murat Tekalp

University of Rochester

53.1 Introduction

53.2 Modeling

Intra-Frame Observation Model • Multispectral Observa￾tion Model • Multiframe Observation Model • Regularization

Models

53.3 Model Parameter Estimation

Blur Identification • Estimation of Regularization Parameters • Estimation of the Noise Variance

53.4 Intra-Frame Restoration

Basic Regularized Restoration Methods • Restoration of Im￾ages Recorded by Nonlinear Sensors • Restoration of Images

Degraded by Random Blurs • Adaptive Restoration for Ring￾ing Reduction • Blind Restoration (Deconvolution) • Restora￾tion of Multispectral Images • Restoration of Space-Varying

Blurred Images

53.5 Multiframe Restoration and Superresolution

Multiframe Restoration • Superresolution • Superresolution

with Space-Varying Restoration

53.6 Conclusion

References

53.1 Introduction

Digital images and video, acquired by still cameras, consumer camcorders, or even broadcast-quality

video cameras, are usually degraded by some amount of blur and noise. In addition, most electronic

camerashave limitedspatialresolutiondeterminedby the characteristicsofthe sensor array. Common

causes of blur are out-of-focus, relative motion, and atmospheric turbulence. Noise sources include

film grain, thermal, electronic, and quantization noise. Further, many image sensors and media have

known nonlinear input-output characteristics which can be represented as point nonlinearities. The

goal of image and video (image sequence) restoration is to estimate each image (frame or field) as it

would appearwithout any degradations, by first modeling the degradation process, and then applying

an inverse procedure. This is distinct from image enhancement techniques which are designed to

manipulate an image in order to produce more pleasing results to an observer without making

use of particular degradation models. On the other hand, superresolution refers to estimating an

image at a resolution higher than that of the imaging sensor. Image sequence filtering (restoration

and superresolution) becomes especially important when still images from video are desired. This

is because the blur and noise can become rather objectionable when observing a “freeze-frame”,

although they may not be visible to the human eye at the usual frame rates. Since many video signals

encountered in practice are interlaced, we address the cases of both progressive and interlaced video.

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