Remini AI老照片画质增强实战

# Remini 工具使用概念示例def remini_enhance(photo):    """一键画质增强：模糊照片 -> 高清照片"""    enhanced = apply_ai_model(photo)  # 调用AI超分辨率模型    return enhanced

# 超分辨率基本流程示意low_res = load_image("blurry.jpg")       # 加载低分辨率图像upsampled = bicubic_upscale(low_res, 2)   # 传统方法上采样high_res = deep_model(upsampled)          # 深度学习恢复细节

# GAN 对抗训练概念generator = Generator()          # 生成器：生成高清细节discriminator = Discriminator()  # 判别器：辨别真伪for epoch in range(100):    fake_hr = generator(low_res)    d_loss = discriminator.train(real_hr, fake_hr)    g_loss = generator.train(fake_hr, discriminator)

# 人脸修复评分示例def face_score(real_face, restored_face):    psnr = compute_psnr(real_face, restored_face)  # 峰值信噪比    ssim = compute_ssim(real_face, restored_face)  # 结构相似性    return {"psnr": psnr, "ssim": ssim}

# 批量老照片修复photos = load_batch("old_photos/")   # 加载批量老照片for photo in photos:    restored = remini_enhance(photo) # 逐张修复    save(restored, f"restored_{photo.name}")

import numpy as np # 1. 模拟超分辨率过程（上采样 + 细节增强）def super_resolution(image, scale=2):    """模拟超分辨率：上采样 + 细节增强"""    h, w = image.shape    new_h, new_w = h * scale, w * scale    upsampled = np.zeros((new_h, new_w))    for i in range(new_h):        for j in range(new_w):            src_i = min(int(i / scale), h - 1)            src_j = min(int(j / scale), w - 1)            upsampled[i, j] = image[src_i, src_j]    enhanced = upsampled * 1.2    enhanced = np.clip(enhanced, 0, 255)    return enhanced # 2. GAN 概念模拟（生成器 + 判别器）class Generator:    """生成器：从低分辨率生成高分辨率细节"""    def __init__(self):        self.weights = np.random.randn(3, 3) * 0.1    def generate(self, low_res_patch):        return low_res_patch + np.sum(self.weights)    def update(self, feedback, lr=0.01):        self.weights += lr * feedback class Discriminator:    """判别器：判断图像是真高清还是生成的"""    def __init__(self):        self.threshold = 0.5    def discriminate(self, patch):        sharpness = np.std(patch)        return 1 if sharpness > self.threshold else 0    def update(self, is_correct, lr=0.01):        self.threshold += lr * (0.1 if is_correct else -0.1) # 3. 人脸修复质量指标def psnr(original, restored):    """计算峰值信噪比 PSNR"""    mse = np.mean((original - restored) ** 2)    if mse == 0:        return float('inf')    return 10 * np.log10(255.0 ** 2 / mse) def face_restoration_score(original, restored):    """人脸修复质量评分"""    psnr_val = psnr(original, restored)    ssim_proxy = 1 - np.mean(np.abs(original - restored)) / 255.0    return {"psnr": round(psnr_val, 2), "ssim_proxy": round(ssim_proxy, 3)} # 4. 图像质量对比（增强前后）def compare_quality(before, after):    """增强前后质量对比"""    before_score = np.std(before)    after_score = np.std(after)    improvement = (after_score - before_score) / before_score * 100    return {"before_sharpness": round(float(before_score), 2),            "after_sharpness": round(float(after_score), 2),            "improvement_pct": round(float(improvement), 2)} # 5. 批量处理模拟def batch_enhance(images, scale=2):    """批量处理模拟"""    results = []    for idx, img in enumerate(images):        enhanced = super_resolution(img, scale)        metric = compare_quality(img, enhanced)        results.append({"index": idx, "metrics": metric})    return results if __name__ == "__main__":    np.random.seed(42)    low_res = np.random.randint(50, 150, (16, 16)).astype(np.float64)    high_res = super_resolution(low_res, scale=2)    gen = Generator()    disc = Discriminator()    fake = gen.generate(low_res[0, 0])    verdict = disc.discriminate(np.array([[fake]]))    print("判别器结果:", "真高清" if verdict else "生成图")    score = face_restoration_score(low_res, high_res[:16, :16])    print("人脸修复评分:", score)    cmp = compare_quality(low_res, high_res)    print("质量对比:", cmp)    batch = [np.random.randint(40, 120, (8, 8)).astype(np.float64) for _ in range(3)]    batch_results = batch_enhance(batch)    print("批量处理结果数:", len(batch_results))