※ 引述《yanqinru (髍鵀)》之铭言： : 各位板友好 : 小弟是刚学影像处理的新手 : 目前在研究杂讯程式碰到了许多问题 : 虽然有对照书本写的杂讯公式来看 : 可是还是不知道以下三段程式是怎麽做加入杂讯运算 老实说小弟有点懒得看他的code了, 因为有些数学计算在里面.... 不同的design思维也许会有不一样的写法, 公式也可能因此变化.... 刚好以前小弟作业写过Noise Generation, 所以就野人献曝一下.... 看您会不会比较容易看懂, 因为我的公式都是书上直接打上来的.... 几年前实作的, 若有漏误的地方, 也请板上其他先进不吝指教.... 先说, 小弟是参考Prentice Hall出版, 作者 Rafael 与 Richard 的 Digital Image Processing 2nd, ISBN: 0-13-094650-8 (原文) http://gist.github.com/93196 这是小弟作业的一个function, 用来将已经输入的gray level影像加入杂讯.... 所以有些变数function内可能没看到宣告, 那是因为放在.h档里.... 我已经尽量注意加补才上传code, 只是像Source/Target Image的data等懒得加.... SourceRaw[]已存好Image的gray level, 只是展开成一维阵列(unsigned char) TargetRaw[]是加入Noise後的Image要存入的地方; TargetSize是总pixel数.... choice是要加入的Noise种类, 有实作的Noise为书本上介Ch5.2 p.224的那六种.... r1与r2是杂讯的参数, 大部份的情况r1为mean, r2为Variance.... 在某些Noise Model参数比较不同, 这点就自己对书看或者直接看注解吧^^|| PS. 请不用理会那个回传值, 那是作业里function的design架构.... == 实作概念: 照书本提供的机率密度函数, 先算出一个pixel变化为原值-255~+255的机率分布(PDF[]) for i从第一到最後一个pixel一遍, 用乱数算出0.0~1.0的机率值, 决定level变化.... 将原始SourceRaw[i]里的level加上变化值, 处理边界判断後, 写进TargetRaw[i]去.... 至於为何需要CPDF[], 是为了将在0.0~1.0的乱数拿去依照原来的PDF[]查表用的.... 实作流程: 1.依照机率模型与其对应函数, 产生PDF[]与CPDF[]. 2.for i产生0.0~1.0的乱数值, 找到对应CPDF的index k. 3.将SourceRaw[i] + k的结果写到TargetRaw[i]并clamp. 4.持续2.直接TargetSize的所有pixels都被处理完. 其他我就不知道要补充什麽了, 当然应该还有其他的design concept.... 不过我的想法, 在产生PDF[]那边, 应该主要计算都match书上的公式.... Impulse小弟我比较没把握, 因为Pa和Pb我不确定是总合为1.0还是各自到1.0.... 它的r1即Pa, r2即Pb, 要是机率密度总合超过1.0, 我就不知道该怎麽算它了Orz == <恕删> : 以上几个问题烦请板友帮小弟解惑 : 不好意思一次问太多问题了... : 小弟仅能尽量以微薄的P币答谢回答问题的板友 : 感谢大家 复习自己写过的一小段程式果然比看别人的程式容易一些XD 如同小弟回覆您mail写的, 要写加入杂讯的功能.... 建议您把Show Histogram的功能先写出来会比较好.... 下面就贴几张贴图好了, 因为我不确定我们是否是同一本书.... 原图(来自书本): http://img382.imageshack.us/img382/2894/fig503l.png 加了Noise之後: http://img408.imageshack.us/img408/7915/noise.png 分别是由原图加入六种Noise, 由左至右给的参数应该是: 1. MV=10, V=100; 2. 同#1; 3. MV=10, V=20; 4.MV=20, V=400; 5. 同#1; 6. Upper Ratio=5%, Lower Ratio=5% == 好像有板友建议也直接贴code, 也好, 贴code网站好像少最後几行@_@" unsigned long ImageProcess::Noise(int choice, double r1, double r2) { //Probability Density Functions and pointers for Noises . double PDF[511], CPDF[511], *P, *CP; //Pick the random number seed by time . time_t t; srand((unsigned) time(&t)); //Compute Probability Density Functions by choice type . switch(choice) { case 1: //Gaussian Noise . { //Use {} to prevent errors for declaring variables in case . double MV, SD, V; //MV: Mean Value; SD: Standard Deviation; V: Variance = SD^2 . MV = r1; V = r2; if(V < 0.) return 0; //Error handle for negative Variance . else SD = sqrt(V); //Compute the probability of a GrayLevel changing its value to other level . if(V != 0.) for(int i=-255; i<=255; i++) P[i] = exp(-(i-MV)*(i-MV)/(2.*V)) / (sqrt(2.*PI)*SD); //Only shift when Variance is zero . else { for(int j=-255; j<=255; j++) P[j] = 0.; if(MV < -255) P[-255] = 1.; else if(MV > 255 || (MV+0.5) > 255) P[255] = 1.; else P[(int)(MV+0.5)] = 1.; } }break; case 2: //Rayleigh Noise . { //Use {} to prevent errors for declaring variables in case . double A, B, MV, V; //MV: Mean Value; V: Variance = SD^2 . MV = r1; V = r2; if(V < 0.) return 0; //Error handle for negative Variance . //Compute the parameters A and B of Rayleigh model . B = 4 * V / (4 - PI); A = MV - sqrt(PI * B / 4); //Compute the probability of a GrayLevel changing its value to other level . for(int i=-255; i<=255; i++) P[i] = i >= A ? 2.*(i-A)*exp(-(i-A)*(i-A)/B) / B : 0.; }break; case 3: //Erlang Noise . { //Use {} to prevent errors for declaring variables in case . double A, MV, V, factorial = 1.; //MV: Mean Value; V: Variance = SD^2 . unsigned long B; MV = r1; V = r2; if(MV <= 0. || V <= 0.) return 0; //Compute the parameters A and B of Erlang model . A = MV / V; B = A * MV + 0.5; A = B / MV; if(A <= 0. || B < 1) return 0; for(unsigned long i=B-1; i>1; i--) factorial *= i; for(int j=-255; j<=0; j++) P[j] = 0.; //Compute the probability of a GrayLevel changing its value to other level . for(int k=1; k<=255; k++) P[k] = (pow(A, B)*pow(k, B-1) / factorial) * exp(-A*k); }break; case 4: //Exponential Noise . { //Use {} to prevent errors for declaring variables in case . double A, MV, V; //MV: Mean Value; V: Variance = SD^2 . MV = r1; if(MV <= 0.) return 0; //Compute the parameters A of Exponential model (B = A^2) . A = 1 / MV; //Compute the probability of a GrayLevel changing its value to other level . for(int i=-255; i<0; i++) P[i] = 0.; for(int j=0; j<=255; j++) P[j] = A * exp(-A*j); }break; case 5: //Uniform Noise . { //Use {} to prevent errors for declaring variables in case . double temp, MV, V; //MV: Mean Value; V: Variance = SD^2 . int A, B; MV = r1; V = r2; if(V < 0.) return 0; //Error handle for negative Variance . //Compute the parameters A and B of Uniform model . temp = MV - sqrt(3. * V); A = temp >= 0. ? temp + 0.5 : temp - 0.5; temp = MV + sqrt(3. * V); B = temp >= 0. ? temp + 0.5 : temp - 0.5; //Compute the probability of a GrayLevel changing its value to other level . if(A != B) for(int i=-255; i<=255; i++) P[i] = (A <= i && i <= B) ? 1. / (B - A) : 0.; //Only shift when Variance is zero . else { for(int j=-255; j<=255; j++) P[j] = 0.; if(A < -255) P[-255] = 1.; else if(B > 255) P[255] = 1.; else P[A] = 1.; } }break; case 6: //Impulse Noise . { //Use {} to prevent errors for declaring variables in case . double A, B, temp; //A, B directly get probability by parameters of Noise() . A = r1; B = r2; if(A < 0. || B < 0.) return 0; //Error handle for negative probability . //Compute the probability of a GrayLevel changing its value to other level . for(int i=-255; i<=255; i++) P[i] = 0.; temp = A + B; if(temp <= 1.) { P[-255] = A; P[0] = 1. - (temp); P[255] = B; } //Special case for the total probability A + B > 1 . else { P[-255] = A / temp; P[255] = B / temp; } }break; default: return 0; //Return 0 for handling error choice type . } //Make Counted Probability tables . CP[-255] = P[-255]; if(choice != 6) { for(int i=-254; i<=255; i++) { CP[i] = CP[i-1] + P[i]; //Special process to prevent errors occured by floating point . if((CP[i] > 0.9995 && CP[i] == CP[i-1]) || CP[i] >= 1.) while(i <= 255) CP[i++] = 1.; } } //Special case for Impulse Noise to prevent error displaying . else { for(int i=-254; i<=255; i++) CP[i] = 0.; CP[0] = CP[-255] + P[0]; CP[255] = CP[0] + P[255]; } //Generate the image added Noise by CPDF[] . int result; //Result of changing Graylevel . unsigned long rv1, rv2, rv3; //Use 3 1-byte random numbers combining 3-bytes random numbers . double RV, R_MAX = 0x00FFFFFFU; //RV: Random Value from 0. to 1; R_MAX: the max of 3-bytes random numbers . for(unsigned long j=0; j<TargetSize; j++) { //Generate 3-bytes random numbers and shrink to 0. ~ 1 . rv1 = rand() & 0x00FFU; rv2 = rand() & 0x00FFU; rv3 = rand() & 0x00FFU; RV = ((rv1<<16) | (rv2<<8) | rv3) / R_MAX; //Extend MAX of RAND form 0x7FFFU to 0x00FFFFFFU . //Generate changing Graylevel . result = SourceRaw[j] + 255; for(int k=-255; k<=254; k++) if(CP[k] > RV) { result = SourceRaw[j] + k; break; } //Fix to 0 ~ 255 and assign to TargetImage . if(result < 0) TargetRaw[j] = 0; else if(result > 255) TargetRaw[j] = 255; else TargetRaw[j] = result; } return TargetSize; } //--------------------------------------------------------------------------- -- 深夜不睡觉在这边骗P币, 早上起不来去拍SG该怎麽办啊....>_<# --

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