使用python提升图片清晰度的常见方法

以下是使用 Python 提升图片清晰度的常见方法及实现代码，涵盖传统图像处理算法和深度学习方案：

一、传统图像增强方法

1. 锐化滤波


import cv2
import numpy as np
def sharpen(img):
    kernel = np.array([[0, -1, 0],
                       [-1, 5, -1],
                       [0, -1, 0]])
    return cv2.filter2D(img, -1, kernel)
img = cv2.imread('blurry.jpg')
sharpened = sharpen(img)
cv2.imwrite('sharpened.jpg', sharpened)
import cv2
import numpy as np

def sharpen(img):
    kernel = np.array([[0, -1, 0],
                       [-1, 5, -1],
                       [0, -1, 0]])
    return cv2.filter2D(img, -1, kernel)

img = cv2.imread('blurry.jpg')
sharpened = sharpen(img)
cv2.imwrite('sharpened.jpg', sharpened)
import cv2
import numpy as np

def sharpen(img):
    kernel = np.array([[0, -1, 0],
                       [-1, 5, -1],
                       [0, -1, 0]])
    return cv2.filter2D(img, -1, kernel)

img = cv2.imread('blurry.jpg')
sharpened = sharpen(img)
cv2.imwrite('sharpened.jpg', sharpened)

2. 非局部均值去噪


def denoise_nlm(img):
    return cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 21)
cleaned = denoise_nlm(img)
def denoise_nlm(img):
    return cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 21)

cleaned = denoise_nlm(img)
def denoise_nlm(img):
    return cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 21)

cleaned = denoise_nlm(img)

3. 超分辨率重建（EDSR 模型）


# 安装依赖：pip install opencv-contrib-python
sr = cv2.dnn_superres.DnnSuperResImpl_create()
sr.readModel('EDSR_x4.pb')  # 需下载预训练模型
sr.setModel('edsr', 4)  # 4倍超分
result = sr.upsample(img)
# 安装依赖：pip install opencv-contrib-python
sr = cv2.dnn_superres.DnnSuperResImpl_create()
sr.readModel('EDSR_x4.pb')  # 需下载预训练模型
sr.setModel('edsr', 4)  # 4倍超分
result = sr.upsample(img)
# 安装依赖：pip install opencv-contrib-python
sr = cv2.dnn_superres.DnnSuperResImpl_create()
sr.readModel('EDSR_x4.pb')  # 需下载预训练模型
sr.setModel('edsr', 4)  # 4倍超分
result = sr.upsample(img)

二、基于深度学习的方案

1. 使用 ESRGAN


from PIL import Image
import torch
from basicsr.archs.rrdbnet_arch import RRDBNet
model = RRDBNet(num_in_ch=3, num_out_ch=3)
model.load_state_dict(torch.load('ESRGAN.pth'))
input_img = Image.open('input.jpg').convert('RGB')
output = model(input_img)  # 需配置预处理
from PIL import Image
import torch
from basicsr.archs.rrdbnet_arch import RRDBNet

model = RRDBNet(num_in_ch=3, num_out_ch=3)
model.load_state_dict(torch.load('ESRGAN.pth'))
input_img = Image.open('input.jpg').convert('RGB')
output = model(input_img)  # 需配置预处理
from PIL import Image
import torch
from basicsr.archs.rrdbnet_arch import RRDBNet

model = RRDBNet(num_in_ch=3, num_out_ch=3)
model.load_state_dict(torch.load('ESRGAN.pth'))
input_img = Image.open('input.jpg').convert('RGB')
output = model(input_img)  # 需配置预处理

2. 实用工具库 Real-ESRGAN


# 安装
pip install realesrgan
# 使用
from realesrgan import RealESRGANer
upsampler = RealESRGANer(scale=4, model_path='RealESRGAN_x4plus.pth')
output, _ = upsampler.enhance(img)
# 安装
pip install realesrgan

# 使用
from realesrgan import RealESRGANer
upsampler = RealESRGANer(scale=4, model_path='RealESRGAN_x4plus.pth')
output, _ = upsampler.enhance(img)
# 安装
pip install realesrgan

# 使用
from realesrgan import RealESRGANer
upsampler = RealESRGANer(scale=4, model_path='RealESRGAN_x4plus.pth')
output, _ = upsampler.enhance(img)

三、频域增强方法

小波变换增强


import pywt
def wavelet_enhance(img):
    # RGB转YCrCb
    ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
    y = ycrcb[:,:,0]
    # 小波分解
    coeffs = pywt.dwt2(y, 'haar')
    cA, (cH, cV, cD) = coeffs
    # 高频增强
    cH *= 1.5
    cV *= 1.5
    cD *= 1.2
    # 重构
    coeffs = cA, (cH, cV, cD)
    y_enhanced = pywt.idwt2(coeffs, 'haar')
    # 合并通道
    ycrcb[:,:,0] = np.clip(y_enhanced, 0, 255)
    return cv2.cvtColor(ycrcb, cv2.COLOR_YCrCb2BGR)
import pywt

def wavelet_enhance(img):
    # RGB转YCrCb
    ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
    y = ycrcb[:,:,0]

    # 小波分解
    coeffs = pywt.dwt2(y, 'haar')
    cA, (cH, cV, cD) = coeffs

    # 高频增强
    cH *= 1.5
    cV *= 1.5
    cD *= 1.2

    # 重构
    coeffs = cA, (cH, cV, cD)
    y_enhanced = pywt.idwt2(coeffs, 'haar')

    # 合并通道
    ycrcb[:,:,0] = np.clip(y_enhanced, 0, 255)
    return cv2.cvtColor(ycrcb, cv2.COLOR_YCrCb2BGR)
import pywt

def wavelet_enhance(img):
    # RGB转YCrCb
    ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
    y = ycrcb[:,:,0]

    # 小波分解
    coeffs = pywt.dwt2(y, 'haar')
    cA, (cH, cV, cD) = coeffs

    # 高频增强
    cH *= 1.5
    cV *= 1.5
    cD *= 1.2

    # 重构
    coeffs = cA, (cH, cV, cD)
    y_enhanced = pywt.idwt2(coeffs, 'haar')

    # 合并通道
    ycrcb[:,:,0] = np.clip(y_enhanced, 0, 255)
    return cv2.cvtColor(ycrcb, cv2.COLOR_YCrCb2BGR)

四、完整处理流程示例


def full_enhance_pipeline(img_path):
    # 读取
    img = cv2.imread(img_path)
    # 1. 去噪
    img = cv2.fastNlMeansDenoisingColored(img, None, 5, 5, 3, 9)
    # 2. 锐化
    kernel = np.array([[-1,-1,-1], 
                      [-1,9,-1],
                      [-1,-1,-1]])
    img = cv2.filter2D(img, -1, kernel)
    # 3. 超分辨率 (需提前下载模型)
    sr = cv2.dnn_superres.DnnSuperResImpl_create()
    sr.readModel('FSRCNN_x4.pb')
    sr.setModel('fsrcnn', 4)
    img = sr.upsample(img)
    # 4. 对比度增强
    lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
    l, a, b = cv2.split(lab)
    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
    l = clahe.apply(l)
    img = cv2.merge((l,a,b))
    img = cv2.cvtColor(img, cv2.COLOR_LAB2BGR)
    return img
def full_enhance_pipeline(img_path):
    # 读取
    img = cv2.imread(img_path)

    # 1. 去噪
    img = cv2.fastNlMeansDenoisingColored(img, None, 5, 5, 3, 9)

    # 2. 锐化
    kernel = np.array([[-1,-1,-1], 
                      [-1,9,-1],
                      [-1,-1,-1]])
    img = cv2.filter2D(img, -1, kernel)

    # 3. 超分辨率 (需提前下载模型)
    sr = cv2.dnn_superres.DnnSuperResImpl_create()
    sr.readModel('FSRCNN_x4.pb')
    sr.setModel('fsrcnn', 4)
    img = sr.upsample(img)

    # 4. 对比度增强
    lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
    l, a, b = cv2.split(lab)
    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
    l = clahe.apply(l)
    img = cv2.merge((l,a,b))
    img = cv2.cvtColor(img, cv2.COLOR_LAB2BGR)

    return img
def full_enhance_pipeline(img_path):
    # 读取
    img = cv2.imread(img_path)

    # 1. 去噪
    img = cv2.fastNlMeansDenoisingColored(img, None, 5, 5, 3, 9)

    # 2. 锐化
    kernel = np.array([[-1,-1,-1], 
                      [-1,9,-1],
                      [-1,-1,-1]])
    img = cv2.filter2D(img, -1, kernel)

    # 3. 超分辨率 (需提前下载模型)
    sr = cv2.dnn_superres.DnnSuperResImpl_create()
    sr.readModel('FSRCNN_x4.pb')
    sr.setModel('fsrcnn', 4)
    img = sr.upsample(img)

    # 4. 对比度增强
    lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
    l, a, b = cv2.split(lab)
    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
    l = clahe.apply(l)
    img = cv2.merge((l,a,b))
    img = cv2.cvtColor(img, cv2.COLOR_LAB2BGR)

    return img

五、方法对比与选型建议

方法	优势	局限性	适用场景
传统锐化	计算快，实时性好	可能放大噪声	轻度模糊，实时处理
频域增强	保留边缘细节	参数调节复杂	纹理丰富的图像
ESRGAN	重建效果最佳	需要GPU，速度慢	严重退化图像
Real-ESRGAN	平衡速度与质量	模型体积大(>100MB)	通用场景
超分辨率模型	分辨率提升明显	需要匹配的预训练模型	低分辨率图像放大