def UNet_wiener(height, width, initial_psf, initial_K, encoding_cs=[24, 64, 128, 256, 512, 1024], center_cs=1024, decoding_cs=[512, 256, 128, 64, 24, 24], skip_connections=[True, True, True, True, True, True]): inputs = tf.keras.Input((height, width, 1)) x = inputs # Multi-Wiener deconvolutions x = WienerDeconvolution(initial_psf, initial_K)(x) skips = [] # Contracting path for c in encoding_cs: x, x_skip = encoder_block(x, c, kernel_size=3, padding='same', dilation_rate=1, pooling='average') skips.append(x_skip) skips = list(reversed(skips)) # Center x = residual_block(x, center_cs, kernel_size=3, padding='same') # Expansive path for i, c in enumerate(decoding_cs): if skip_connections[i]: x = decoder_block_resize(x, skips[i], c, kernel_size=3, padding='same', dilation_rate=1) else: x = decoder_block(x, None, c, kernel_size=3, padding='same', dilation_rate=1) # Classify x = layers.Conv2D(filters=1, kernel_size=1, use_bias=True, activation='relu')(x) outputs = tf.squeeze(x, axis=3) model = tf.keras.Model(inputs=[inputs], outputs=[outputs]) return model

这段代码看起来像是一个基于 UNet 的神经网络模型，用于图像去噪。其中包括了一个 Multi-Wiener Deconvolution 模块，用于去除图像中的模糊和噪声。模型的编码器和解码器都使用了卷积层和反卷积层，并且采用了 skip connections 技术，以帮助信息传递和提高模型性能。最后的输出是一个二维图像，经过了 squeeze 操作，去掉了 channel 维度，变成了一个灰度图像。