Xử lý hình ảnh kỹ thuật số P12

POINT AND SPATIAL IMAGE RESTORATION TECHNIQUESA common defect in imaging systems is unwanted nonlinearities in the sensor and display systems. Post processing correction of sensor signals and pre-processing correction of display signals can reduce such degradations substantially (1). Such point restoration processing is usually relatively simple to implement. One of the most common image restoration tasks is that of spatial image restoration to compensate for image blur and to diminish noise effects. References 2 to 6 contain surveys of spatial image restoration methods....
Nội dung trích xuất từ tài liệu:
Xử lý hình ảnh kỹ thuật số P12 Digital Image Processing: PIKS Inside, Third Edition. William K. Pratt Copyright © 2001 John Wiley & Sons, Inc. ISBNs: 0-471-37407-5 (Hardback); 0-471-22132-5 (Electronic)12POINT AND SPATIAL IMAGERESTORATION TECHNIQUESA common defect in imaging systems is unwanted nonlinearities in the sensor anddisplay systems. Post processing correction of sensor signals and pre-processingcorrection of display signals can reduce such degradations substantially (1). Suchpoint restoration processing is usually relatively simple to implement. One of themost common image restoration tasks is that of spatial image restoration to compen-sate for image blur and to diminish noise effects. References 2 to 6 contain surveysof spatial image restoration methods.12.1. SENSOR AND DISPLAY POINT NONLINEARITY CORRECTIONThis section considers methods for compensation of point nonlinearities of sensorsand displays.12.1.1. Sensor Point Nonlinearity CorrectionIn imaging systems in which the source degradation can be separated into cascadedspatial and point effects, it is often possible directly to compensate for the point deg-radation (7). Consider a physical imaging system that produces an observed imagefield FO ( x, y ) according to the separable model F O ( x, y ) = O Q { O D { C ( x, y, λ ) } } (12.1-1) 319320 POINT AND SPATIAL IMAGE RESTORATION TECHNIQUES FIGURE 12.1-1. Point luminance correction for an image sensor.where C ( x, y, λ ) is the spectral energy distribution of the input light field, OQ { · }represents the point amplitude response of the sensor and O D { · } denotes the spatialand wavelength responses. Sensor luminance correction can then be accomplishedby passing the observed image through a correction system with a point restorationoperator O R { · } ideally chosen such that OR { OQ { · } } = 1 (12.1-2)For continuous images in optical form, it may be difficult to implement a desiredpoint restoration operator if the operator is nonlinear. Compensation for images inanalog electrical form can be accomplished with a nonlinear amplifier, while digitalimage compensation can be performed by arithmetic operators or by a table look-upprocedure. Figure 12.1-1 is a block diagram that illustrates the point luminance correctionmethodology. The sensor input is a point light distribution function C that is con-verted to a binary number B for eventual entry into a computer or digital processor.In some imaging applications, processing will be performed directly on the binaryrepresentation, while in other applications, it will be preferable to convert to a realfixed-point computer number linearly proportional to the sensor input luminance. In ˜the former case, the binary correction unit will produce a binary number B that isdesigned to be linearly proportional to C, and in the latter case, the fixed-point cor- ˜rection unit will produce a fixed-point number C that is designed to be equal to C. A typical measured response B versus sensor input luminance level C is shown inFigure 12.1-2a, while Figure 12.1-2b shows the corresponding compensatedresponse that is desired. The measured response can be obtained by scanning a grayscale test chart of known luminance values and observing the digitized binary valueB at each step. Repeated measurements should be made to reduce the effects ofnoise and measurement errors. For calibration purposes, it is convenient to regardthe binary-coded luminance as a fixed-point binary number. As an example, if theluminance range is sliced to 4096 levels and coded with 12 bits, the binary represen-tation would be B = b8 b7 b6 b5 b4 b3 b2 b1. b–1 b–2 b–3 b–4 (12.1-3) SENSOR AND DISPLAY POINT NONLINEARITY CORRECTION 321 FIGURE 12.1-2. Measured and compensated sensor luminance response.The whole-number part in this example ranges from 0 to 255, and the fractional partdivides each integer step into 16 subdivisions. In this format, the scanner can pro-duce output levels over the range 255.9375 ≤ B ≤ 0.0 (12.1-4) After the measured gray scale data points of Figure 12.1-2a have been obtained, asmooth analytic curve C = g{B} (12.1-5)is fitted to the data. The desired luminance response in real number and binary num-ber forms is322 POINT AND SPATIAL IMAGE RESTORATION TECHNIQUES ˜ C = C (12.1-6a) ˜ C – C min B = B max ------------------------------ - (12.1-6b) C max – C minHence, the required compensation relationships are ˜ C = g{ B} (12.1-7a) ˜ g { B } – C min B = B max ------------------------------- (12.1-7b) C max – C minThe limits of the luminance function are commonly normalized to the range 0.0 to1.0. To improve the accuracy of the calibration procedure, it is first wise to perform arough calibration and then rep ...