如何提高图像质量？ [关闭]

Posted 2023-04-17

【中文标题】如何提高图像质量？ [关闭]

【英文标题】：How to improve image quality? [closed]

【发布时间】：2019-01-30 21:58:06

【问题描述】：

我正在制作一个读取身份证的 OCR。通过使用 YOLO 获得感兴趣的区域后，我将该裁剪区域提供给 Tesseract 以读取它。由于这些裁剪的图像非常小且模糊，Tesseract 无法读取它们。当它可以阅读它们时，它会给出错误的预测。我认为通过提高裁剪图像的图像质量，可以解决这些问题。

裁剪后的图像之一：

我的问题是，我将如何改进这些图像？

【问题讨论】：

对于提高图像质量所必须使用的工具是否有任何限制？

我认为没有限制

【参考方案1】：

@vasilisg 的回答。是一个非常好的解决方案。进一步改进这一点的一种方法是使用形态学打开操作去除剩余的斑点。但是，这仅适用于小于图像中数字线厚的点。另一种选择是使用 openCV 连接组件模块删除小于 N 像素的“孤岛”。例如，您可以这样做：

# External libraries used for
# Image IO
from PIL import Image

# Morphological filtering
from skimage.morphology import opening
from skimage.morphology import disk

# Data handling
import numpy as np

# Connected component filtering
import cv2

black = 0
white = 255
threshold = 160

# Open input image in grayscale mode and get its pixels.
img = Image.open("image.jpg").convert("LA")
pixels = np.array(img)[:,:,0]

# Remove pixels above threshold
pixels[pixels > threshold] = white
pixels[pixels < threshold] = black


# Morphological opening
blobSize = 1 # Select the maximum radius of the blobs you would like to remove
structureElement = disk(blobSize)  # you can define different shapes, here we take a disk shape
# We need to invert the image such that black is background and white foreground to perform the opening
pixels = np.invert(opening(np.invert(pixels), structureElement))


# Create and save new image.
newImg = Image.fromarray(pixels).convert('RGB')
newImg.save("newImage1.PNG")

# Find the connected components (black objects in your image)
# Because the function searches for white connected components on a black background, we need to invert the image
nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(np.invert(pixels), connectivity=8)

# For every connected component in your image, you can obtain the number of pixels from the stats variable in the last
# column. We remove the first entry from sizes, because this is the entry of the background connected component
sizes = stats[1:,-1]
nb_components -= 1

# Define the minimum size (number of pixels) a component should consist of
minimum_size = 100

# Create a new image
newPixels = np.ones(pixels.shape)*255

# Iterate over all components in the image, only keep the components larger than minimum size
for i in range(1, nb_components):
    if sizes[i] > minimum_size:
        newPixels[output == i+1] = 0

# Create and save new image.
newImg = Image.fromarray(newPixels).convert('RGB')
newImg.save("newImage2.PNG")

在这个例子中，我已经执行了打开和连接组件方法，但是如果您使用连接组件方法，您通常可以省略打开操作。

结果如下所示：

阈值化和打开后：

阈值化、打开和连通分量过滤后：

【参考方案2】：

一种方法是将图像转换为灰度，然后使用阈值与每个像素进行比较，以确定它应该是黑色还是白色。 Pillow 是一个可用于此类处理的库：

from PIL import Image

black = (0,0,0)
white = (255,255,255)
threshold = (160,160,160)

# Open input image in grayscale mode and get its pixels.
img = Image.open("image.jpg").convert("LA")
pixels = img.getdata()

newPixels = []

# Compare each pixel 
for pixel in pixels:
    if pixel < threshold:
        newPixels.append(black)
    else:
        newPixels.append(white)

# Create and save new image.
newImg = Image.new("RGB",img.size)
newImg.putdata(newPixels)
newImg.save("newImage.jpg")

结果图片：