Image ProcessingWhat is a Digital Image?Limitations on Digital ImagesImage ProcessingSimilar to Analog / ContinuousAccount for Limitations of DigitalInherently New Digital OperationsDigital Image ProcessingAdjusting BrightnessAdjusting ContrastDigression: Perception of IntensityModeling Nonlinear Intensity ResponseCamerasCRT ResponseCCD CamerasDigital Image ProcessingBasic Operation: ConvolutionConvolution with a Triangle FilterConvolution with a Triangle FilterConvolution with a Triangle FilterConvolution with a Triangle FilterConvolution with a Gaussian FilterLinear FilteringLinear FilteringLinear FilteringLinear FilteringLinear FilteringBlurEdge DetectionSharpenEmbossNon-Linear FilteringDigital Image ProcessingQuantizationUniform QuantizationUniform QuantizationReducing Effects of QuantizationDitheringRandom DitherRandom DitherOrdered DitherOrdered DitherOrdered DitherError Diffusion DitherError Diffusion DitherReducing Effects of QuantizationClassical HalftoningClassical HalftoningDigital Halftone PatternsSummaryNext Time…Image Processing COS 426What is a Digital Image? A digital image is a discrete array of samples representing a continuous 2D function Continuous function Discrete samplesLimitations on Digital Images • Spatial discretization • Quantized intensity • Approximate color (RGB) • (Temporally discretized frames for digital video)Image Processing • Changing intensity/color Linear: scale, offset, etc. Nonlinear: gamma, saturation, etc. Add random noise • Filtering over neighborhoods Blur Detect edges Sharpen Emboss Median • Moving image locations Scale Rotate Warp • Combining images Composite MorphSimilar to Analog / Continuous • Changing intensity/color Linear: scale, offset, etc. Nonlinear: gamma, saturation, etc. Add random noise • Filtering over neighborhoods Blur Detect edges Sharpen Emboss Median • Moving image locations Scale Rotate Warp • Combining images Composite MorphAccount for Limitations of Digital • Changing intensity/color Linear: scale, offset, etc. Nonlinear: gamma, saturation, etc. Add random noise • Filtering over neighborhoods Blur Detect edges Sharpen Emboss Median • Moving image locations Scale Rotate Warp • Combining images Composite MorphInherently New Digital Operations • Changing intensity/color Linear: scale, offset, etc. Nonlinear: gamma, saturation, etc. Add random noise • Filtering over neighborhoods Blur Detect edges Sharpen Emboss Median • Moving image locations Scale Rotate Warp • Combining images Composite Morph • Quantization • Spatial / intensity tradeoff DitheringDigital Image Processing • Changing intensity/color Linear: scale, offset, etc. Nonlinear: gamma, saturation, etc. Add random noise • Filtering over neighborhoods Blur Detect edges Sharpen Emboss Median • Moving image locations Scale Rotate Warp • Combining images Composite Morph • Quantization • Spatial / intensity tradeoff DitheringAdjusting Brightness • Simply scale pixel components o Must clamp to range, e.g. [0..1] or [0..255] Original Brighter Note: this is “contrast” on your monitor! “Brightness” adjusts black level (offset)Adjusting Contrast • Compute mean luminance L for all pixels o luminance = 0.30*r + 0.59*g + 0.11*b • Scale deviation from L for each pixel component o Must clamp to range (e.g., 0 to 1) Original More Contrast LDigression: Perception of Intensity • Perception of intensity is nonlinear Amount of light Perceived brightnessModeling Nonlinear Intensity Response • Brightness (B) usually modeled as a logarithm or power law of intensity (I) • Exact curve varies with ambient light, adaptation of eye 3/1logIBIkB==I BCameras • Original cameras based on Vidicon obey power law for Voltage (V) vs. Intensity (I): 45.0≈=γγIVCRT Response • Power law for Intensity (I) vs. applied voltage (V) • Vidicon + CRT = almost linear! • Other displays (e.g. LCDs) contain electronics to emulate this law 5.2≈=γγVICCD Cameras • Camera gamma codified in NTSC standard • CCDs have linear response to incident light •Electronics to apply required power law • So, pictures from most cameras (including digital still cameras) will have γ = 0.45 sRGB standard: partly-linear, partly power-law curve well approximated by γ = 1 / 2.2Digital Image Processing • Changing intensity/color Linear: scale, offset, etc. Nonlinear: gamma, saturation, etc. Add random noise • Filtering over neighborhoods Blur Detect edges Sharpen Emboss Median • Moving image locations Scale Rotate Warp • Combining images Composite Morph • Quantization • Spatial / intensity tradeoff DitheringBasic Operation: Convolution Output value is weighted sum of values in neighborhood of input image Pattern of weights is the “filter” or “kernel” Input Filter OutputConvolution with a Triangle Filter Input Output Filter 0.5 0.25 0.25Convolution with a Triangle Filter Input Output Filter 0.5 0.25 0.25Convolution with a Triangle Filter What if the filter runs off the end? Input Output Filter 0.5 0.25 0.25Convolution with a Triangle Filter Common option: normalize the filter Input Output 0.67 Modified Filter 0.33Convolution with a Gaussian Filter Input Output Figure 2.4 Wolberg FilterLinear Filtering 2D Convolution o Each output pixel is a linear combination of input pixels in neighborhood with weights prescribed by a filter Input Image Filter Output ImageLinear Filtering 2D Convolution o Each output pixel is a linear combination of input pixels in neighborhood with weights prescribed by a filter Input Image Filter Output ImageLinear Filtering 2D Convolution o Each output pixel is a linear combination of input pixels in neighborhood with weights prescribed by a filter Input Image Filter Output ImageLinear Filtering 2D Convolution o Each output pixel is a linear combination of input pixels in neighborhood with weights prescribed by a filter Input Image Filter Output ImageLinear Filtering 2D Convolution o Each output pixel is a linear combination of input pixels in neighborhood with weights prescribed by a filter Input Image Filter Output ImageBlur Convolve with a
View Full Document
Unlocking...