python實現圖像增強算法
本文實例為大傢分享瞭python實現圖像增強算法的具體代碼,供大傢參考,具體內容如下
圖像增強算法,圖像銳化算法
1)基於直方圖均衡化
2)基於拉普拉斯算子
3)基於對數變換
4)基於伽馬變換
5) CLAHE
6)retinex-SSR
7)retinex-MSR
其中,基於拉普拉斯算子的圖像增強為利用空域卷積運算實現濾波
基於同一圖像對比增強效果
直方圖均衡化:對比度較低的圖像適合使用直方圖均衡化方法來增強圖像細節
拉普拉斯算子可以增強局部的圖像對比度
log對數變換對於整體對比度偏低並且灰度值偏低的圖像增強效果較好
伽馬變換對於圖像對比度偏低,並且整體亮度值偏高(對於相機過曝)情況下的圖像增強效果明顯
import cv2 import numpy as np import matplotlib.pyplot as plt # 直方圖均衡增強 def hist(image): r, g, b = cv2.split(image) r1 = cv2.equalizeHist(r) g1 = cv2.equalizeHist(g) b1 = cv2.equalizeHist(b) image_equal_clo = cv2.merge([r1, g1, b1]) return image_equal_clo # 拉普拉斯算子 def laplacian(image): kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]]) image_lap = cv2.filter2D(image, cv2.CV_8UC3, kernel) # cv2.imwrite('th1.jpg', image_lap) return image_lap # 對數變換 def log(image): image_log = np.uint8(np.log(np.array(image) + 1)) cv2.normalize(image_log, image_log, 0, 255, cv2.NORM_MINMAX) # 轉換成8bit圖像顯示 cv2.convertScaleAbs(image_log, image_log) return image_log # 伽馬變換 def gamma(image): fgamma = 2 image_gamma = np.uint8(np.power((np.array(image) / 255.0), fgamma) * 255.0) cv2.normalize(image_gamma, image_gamma, 0, 255, cv2.NORM_MINMAX) cv2.convertScaleAbs(image_gamma, image_gamma) return image_gamma # 限制對比度自適應直方圖均衡化CLAHE def clahe(image): b, g, r = cv2.split(image) clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) b = clahe.apply(b) g = clahe.apply(g) r = clahe.apply(r) image_clahe = cv2.merge([b, g, r]) return image_clahe def replaceZeroes(data): min_nonzero = min(data[np.nonzero(data)]) data[data == 0] = min_nonzero return data # retinex SSR def SSR(src_img, size): L_blur = cv2.GaussianBlur(src_img, (size, size), 0) img = replaceZeroes(src_img) L_blur = replaceZeroes(L_blur) dst_Img = cv2.log(img/255.0) dst_Lblur = cv2.log(L_blur/255.0) dst_IxL = cv2.multiply(dst_Img, dst_Lblur) log_R = cv2.subtract(dst_Img, dst_IxL) dst_R = cv2.normalize(log_R,None, 0, 255, cv2.NORM_MINMAX) log_uint8 = cv2.convertScaleAbs(dst_R) return log_uint8 def SSR_image(image): size = 3 b_gray, g_gray, r_gray = cv2.split(image) b_gray = SSR(b_gray, size) g_gray = SSR(g_gray, size) r_gray = SSR(r_gray, size) result = cv2.merge([b_gray, g_gray, r_gray]) return result # retinex MMR def MSR(img, scales): weight = 1 / 3.0 scales_size = len(scales) h, w = img.shape[:2] log_R = np.zeros((h, w), dtype=np.float32) for i in range(scales_size): img = replaceZeroes(img) L_blur = cv2.GaussianBlur(img, (scales[i], scales[i]), 0) L_blur = replaceZeroes(L_blur) dst_Img = cv2.log(img/255.0) dst_Lblur = cv2.log(L_blur/255.0) dst_Ixl = cv2.multiply(dst_Img, dst_Lblur) log_R += weight * cv2.subtract(dst_Img, dst_Ixl) dst_R = cv2.normalize(log_R,None, 0, 255, cv2.NORM_MINMAX) log_uint8 = cv2.convertScaleAbs(dst_R) return log_uint8 def MSR_image(image): scales = [15, 101, 301] # [3,5,9] b_gray, g_gray, r_gray = cv2.split(image) b_gray = MSR(b_gray, scales) g_gray = MSR(g_gray, scales) r_gray = MSR(r_gray, scales) result = cv2.merge([b_gray, g_gray, r_gray]) return result if __name__ == "__main__": image = cv2.imread('img/FJ(93).png') image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) plt.subplot(4, 2, 1) plt.imshow(image) plt.axis('off') plt.title('Offical') # 直方圖均衡增強 image_equal_clo = hist(image) plt.subplot(4, 2, 2) plt.imshow(image_equal_clo) plt.axis('off') plt.title('equal_enhance') # 拉普拉斯算法增強 image_lap = laplacian(image) plt.subplot(4, 2, 3) plt.imshow(image_lap) plt.axis('off') plt.title('laplacian_enhance') # LoG對象算法增強 image_log = log(image) plt.subplot(4, 2, 4) plt.imshow(image_log) plt.axis('off') plt.title('log_enhance') # # 伽馬變換 image_gamma = gamma(image) plt.subplot(4, 2, 5) plt.imshow(image_gamma) plt.axis('off') plt.title('gamma_enhance') # CLAHE image_clahe = clahe(image) plt.subplot(4, 2, 6) plt.imshow(image_clahe) plt.axis('off') plt.title('CLAHE') # retinex_ssr image_ssr = SSR_image(image) plt.subplot(4, 2, 7) plt.imshow(image_ssr) plt.axis('off') plt.title('SSR') # retinex_msr image_msr = MSR_image(image) plt.subplot(4, 2, 8) plt.imshow(image_msr) plt.axis('off') plt.title('MSR') plt.show()
以上就是本文的全部內容,希望對大傢的學習有所幫助,也希望大傢多多支持WalkonNet。
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