代码阅读deblurgan

本文探讨了layers_utils.py中的ReflectionPadding2D和perceptual_loss函数,介绍了如何在模型中应用反射填充和计算损失。generator_model与discriminator_model展示了卷积网络在生成器和判别器中的应用,涉及ResNet块和LeakyReLU。重点涵盖了VGG16模型的使用和losses.py中的L1、感知损失和Wasserstein损失。同时,训练流程中包含了权重保存和图像去模糊技术的实现。
一.layers_utils.py
1.ReflectionPadding2D():该层可以添加行、列或零在图像张量的顶部、底部、左侧和右侧。整数:相同的对称填充应用于宽度和高度。两个整数的元组:解释为两个不同的高度和宽度的对称填充值。数据形式为字符串,默认是 channels_last 。输入四维张量

2.spatial_reflection_2d_padding(x, padding=((1, 1), (1, 1)), data_format=None):填充四维向量的第二第三维度,返回tf张量 tf.pad(x, pattern, "REFLECT")。padding整数张量【秩,2】。如果模型是reflect,填充[D,0],[D,1]

。其中加判断数据格式是否是data-format的语句,first\last\none。

assert len() 断言函数

assert语句用来声明某个条件是真的,当assert语句失败的时候,会引发一AssertionError.

3. class ReflectionPadding2D(Layer)

(1)python 中可变参数的两种形式,并且 *args(元组)必须放在 **kwargs( keyword arguments 的缩写) 的前面,因为位置参数在关键字参数的前面. **kwargs将一个可变的关键字参数的字典传给函数实参,同样参数

3D Gaussian Splatting (3DGS) has recently emerged as a significant advancement in reconstructing high-quality 3D scenes from video sequences due to its impressive speed and rendering quality [1]. Despite these advantages, 3DGS heavily relies on the quality of the input video frames, making it sensitive to issues such as redundant frames resulting from slow camera movements and severe motion blur caused by rapid movements. Such inconsistencies in video data can significantly degrade the accuracy and efficiency of the 3D reconstruction process. Additionally, the widespread adoption of 3DGS has traditionally been hampered by the assumption of requiring high-end computational resources, such as powerful GPUs. This resource-intensive approach limits the method's applicability, especially in scenarios where accessibility and cost-effectiveness are critical considerations. To address these challenges, this study proposes efficient and fast preprocessing techniques specifically designed to enhance the quality of the input data and streamline the 3D reconstruction pipeline. Our preprocessing framework integrates perceptual hashing (dHash) for frame deduplication [2], Laplacian filtering for detecting motion-blurred frames [3], and a high-performance deblurring method, DeblurGAN-v2 [4], to significantly improve frame clarity before reconstruction. The proposed approach has been carefully optimized to operate effectively on moderate computing hardware, making advanced 3D reconstruction techniques accessible to a broader user base. Empirical results demonstrate that our preprocessing methods not only enhance the reconstruction quality, as evidenced by increased Peak Signal-to-Noise Ratio (PSNR) and reduced L1 Loss, but also markedly improve computational efficiency. Notably, preprocessing time remains minimal—typically under 3 minutes per video—while the runtime savings during the reconstruction process far exceed the additional preprocessing time investment. This combination of improved performance and reduced computational demands facilitates broader adoption and practical usage of 3DGS across diverse applications. 以上這篇的Ai生成占比太高,幫我潤飾一下語句並不要破壞格式
最新发布
06-24
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