图像增强技术英文版.ppt

Feature Extraction Lecture 7 Image Enhancement What are Image Features? Local, meaningful, detectable parts of the image. More Color Woes Squares with dots in them are the same color Topics Image Enhancement Brightness mapping Contrast stretching/enhancement Histogram modification Noise Reduction ……... Mathematical Techniques Convolution Gaussian Filtering Edge and Line Detection and Extraction Region Segmentation Contour Extraction Corner Detection Image Enhancement Goal: improve the ‘visual quality’ of the image for human viewing for subsequent processing Two typical methods spatial domain techniques.... operate directly on image pixels frequency domain techniques.... operate on the Fourier transform of the image No general theory of ‘visual quality’ General assumption: if it looks better, it is better Often not a good assumption Spatial Domain Methods Transformation T point - pixel to pixel area - local area to pixel global - entire image to pixel Neighborhoods typically rectangular typically an odd size: 3x3, 5x5, etc centered on pixel I(x,y) Many IP algorithms rely on this basic notion Point Transforms: General Idea O = T(I) Grayscale Transforms Photoshop ‘adjust curve’ command Point Transforms: Brightness Point Transforms:Thresholding T is a point-to-point transformation only information at I(x,y) used to generate I’(x,y) Thresholding Point Transforms: Linear Stretch Linear Scaling Consider the case where the original image only utilizes a small subset of the full range of gray values: New image uses full range of gray values. Whats F? {just the equation of the straight line} Linear Scaling F is the equation of the straight line going through the point (Imin , 0) and (Imax , K) useful when the image gray values do not fill the available range. Implement via lookup tables Scaling Discrete Images Have assumed a continuous grayscale. What happens in the case of a discrete grayscale with K levels? Examples Non-Linear Scaling: Power Law ? 1 to enhance co