""" Title: Zero-DCE for low-light image enhancement Author: [Soumik Rakshit](http://github.com/soumik12345) Date created: 2021/09/18 Last modified: 2021/09/19 Description: Implementing Zero-Reference Deep Curve Estimation for low-light image enhancement. Accelerator: GPU """ """ ## Introduction **Zero-Reference Deep Curve Estimation** or **Zero-DCE** formulates low-light image enhancement as the task of estimating an image-specific [*tonal curve*](https://en.wikipedia.org/wiki/Curve_(tonality)) with a deep neural network. In this example, we train a lightweight deep network, **DCE-Net**, to estimate pixel-wise and high-order tonal curves for dynamic range adjustment of a given image. Zero-DCE takes a low-light image as input and produces high-order tonal curves as its output. These curves are then used for pixel-wise adjustment on the dynamic range of the input to obtain an enhanced image. The curve estimation process is done in such a way that it maintains the range of the enhanced image and preserves the contrast of neighboring pixels. This curve estimation is inspired by curves adjustment used in photo editing software such as Adobe Photoshop where users can adjust points throughout an image’s tonal range. Zero-DCE is appealing because of its relaxed assumptions with regard to reference images: it does not require any input/output image pairs during training. This is achieved through a set of carefully formulated non-reference loss functions, which implicitly measure the enhancement quality and guide the training of the network. ### References - [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://arxiv.org/pdf/2001.06826.pdf) - [Curves adjustment in Adobe Photoshop](https://helpx.adobe.com/photoshop/using/curves-adjustment.html) """ """ ## Downloading LOLDataset The **LoL Dataset** has been created for low-light image enhancement. It provides 485 images for training and 15 for testing. Each image pair in the dataset consists of a low-light input image and its corresponding well-exposed reference image. """ import os import random import numpy as np from glob import glob from PIL import Image, ImageOps import matplotlib.pyplot as plt import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers """shell gdown https://drive.google.com/uc?id=1DdGIJ4PZPlF2ikl8mNM9V-PdVxVLbQi6 unzip -q lol_dataset.zip """ """ ## Creating a TensorFlow Dataset We use 300 low-light images from the LoL Dataset training set for training, and we use the remaining 185 low-light images for validation. We resize the images to size `256 x 256` to be used for both training and validation. Note that in order to train the DCE-Net, we will not require the corresponding enhanced images. """ IMAGE_SIZE = 256 BATCH_SIZE = 16 MAX_TRAIN_IMAGES = 400 def load_data(image_path): # print(image_path) image = tf.io.read_file(image_path) image = tf.image.decode_png(image, channels=3) image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE]) image = image / 255.0 return image def data_generator(low_light_images): dataset = tf.data.Dataset.from_tensor_slices((low_light_images)) dataset = dataset.map(load_data) dataset = dataset.batch(BATCH_SIZE, drop_remainder=True) return dataset """数据集地址给""" train_low_light_images = sorted(glob("./Zero/eval485/low/*"))[:MAX_TRAIN_IMAGES] val_low_light_images = sorted(glob("./Zero/eval485/low/*"))[MAX_TRAIN_IMAGES:] test_low_light_images = sorted(glob("./Zero/our15/low/*")) train_dataset = data_generator(train_low_light_images) val_dataset = data_generator(val_low_light_images) print("Train Dataset:", train_dataset) print("Validation Dataset:", val_dataset) """ ## The Zero-DCE Framework The goal of DCE-Net is to estimate a set of best-fitting light-enhancement curves (LE-curves) given an input image. The framework then maps all pixels of the input’s RGB channels by applying the curves iteratively to obtain the final enhanced image. ### Understanding light-enhancement curves A ligh-enhancement curve is a kind of curve that can map a low-light image to its enhanced version automatically, where the self-adaptive curve parameters are solely dependent on the input image. When designing such a curve, three objectives should be taken into account: - Each pixel value of the enhanced image should be in the normalized range `[0,1]`, in order to avoid information loss induced by overflow truncation. - It should be monotonous, to preserve the contrast between neighboring pixels. - The shape of this curve should be as simple as possible, and the curve should be differentiable to allow backpropagation. The light-enhancement curve is separately applied to three RGB channels instead of solely on the illumination channel. The three-channel adjustment can better preserve the inherent color and reduce the risk of over-saturation.  ### DCE-Net The DCE-Net is a lightweight deep neural network that learns the mapping between an input image and its best-fitting curve parameter maps. The input to the DCE-Net is a low-light image while the outputs are a set of pixel-wise curve parameter maps for corresponding higher-order curves. It is a plain CNN of seven convolutional layers with symmetrical concatenation. Each layer consists of 32 convolutional kernels of size 3×3 and stride 1 followed by the ReLU activation function. The last convolutional layer is followed by the Tanh activation function, which produces 24 parameter maps for 8 iterations, where each iteration requires three curve parameter maps for the three channels.  """ def build_dce_net(): input_img = keras.Input(shape=[256, 256, 3]) conv1 = layers.Conv2D( 32, (3, 3), strides=(1, 1), activation="relu", padding="same" )(input_img) conv2 = layers.Conv2D( 32, (3, 3), strides=(1, 1), activation="relu", padding="same" )(conv1) conv3 = layers.Conv2D( 32, (3, 3), strides=(1, 1), activation="relu", padding="same" )(conv2) conv4 = layers.Conv2D( 32, (3, 3), strides=(1, 1), activation="relu", padding="same" )(conv3) int_con1 = layers.Concatenate(axis=-1)([conv4, conv3]) conv5 = layers.Conv2D( 32, (3, 3), strides=(1, 1), activation="relu", padding="same" )(int_con1) int_con2 = layers.Concatenate(axis=-1)([conv5, conv2]) conv6 = layers.Conv2D( 32, (3, 3), strides=(1, 1), activation="relu", padding="same" )(int_con2) int_con3 = layers.Concatenate(axis=-1)([conv6, conv1]) x_r = layers.Conv2D(24, (3, 3), strides=(1, 1), activation="tanh", padding="same")( int_con3 ) return keras.Model(inputs=input_img, outputs=x_r) """ ## Loss functions To enable zero-reference learning in DCE-Net, we use a set of differentiable zero-reference losses that allow us to evaluate the quality of enhanced images. """ """ ### Color constancy loss The *color constancy loss* is used to correct the potential color deviations in the enhanced image. """ def color_constancy_loss(x): mean_rgb = tf.reduce_mean(x, axis=(1, 2), keepdims=True) mr, mg, mb = mean_rgb[:, :, :, 0], mean_rgb[:, :, :, 1], mean_rgb[:, :, :, 2] d_rg = tf.square(mr - mg) d_rb = tf.square(mr - mb) d_gb = tf.square(mb - mg) return tf.sqrt(tf.square(d_rg) + tf.square(d_rb) + tf.square(d_gb)) """ ### Exposure loss To restrain under-/over-exposed regions, we use the *exposure control loss*. It measures the distance between the average intensity value of a local region and a preset well-exposedness level (set to `0.6`). """ def exposure_loss(x, mean_val=0.6): x = tf.reduce_mean(x, axis=3, keepdims=True) mean = tf.nn.avg_pool2d(x, ksize=16, strides=16, padding="VALID") return tf.reduce_mean(tf.square(mean - mean_val)) """ ### Illumination smoothness loss To preserve the monotonicity relations between neighboring pixels, the *illumination smoothness loss* is added to each curve parameter map. """ def illumination_smoothness_loss(x): batch_size = tf.shape(x)[0] h_x = tf.shape(x)[1] w_x = tf.shape(x)[2] count_h = (tf.shape(x)[2] - 1) * tf.shape(x)[3] count_w = tf.shape(x)[2] * (tf.shape(x)[3] - 1) h_tv = tf.reduce_sum(tf.square((x[:, 1:, :, :] - x[:, : h_x - 1, :, :]))) w_tv = tf.reduce_sum(tf.square((x[:, :, 1:, :] - x[:, :, : w_x - 1, :]))) batch_size = tf.cast(batch_size, dtype=tf.float32) count_h = tf.cast(count_h, dtype=tf.float32) count_w = tf.cast(count_w, dtype=tf.float32) return 2 * (h_tv / count_h + w_tv / count_w) / batch_size """ ### Spatial consistency loss The *spatial consistency loss* encourages spatial coherence of the enhanced image by preserving the contrast between neighboring regions across the input image and its enhanced version. """ class SpatialConsistencyLoss(keras.losses.Loss): def __init__(self, **kwargs): super().__init__(reduction="none") self.left_kernel = tf.constant( [[[[0, 0, 0]], [[-1, 1, 0]], [[0, 0, 0]]]], dtype=tf.float32 ) self.right_kernel = tf.constant( [[[[0, 0, 0]], [[0, 1, -1]], [[0, 0, 0]]]], dtype=tf.float32 ) self.up_kernel = tf.constant( [[[[0, -1, 0]], [[0, 1, 0]], [[0, 0, 0]]]], dtype=tf.float32 ) self.down_kernel = tf.constant( [[[[0, 0, 0]], [[0, 1, 0]], [[0, -1, 0]]]], dtype=tf.float32 ) def call(self, y_true, y_pred): original_mean = tf.reduce_mean(y_true, 3, keepdims=True) enhanced_mean = tf.reduce_mean(y_pred, 3, keepdims=True) original_pool = tf.nn.avg_pool2d( original_mean, ksize=4, strides=4, padding="VALID" ) enhanced_pool = tf.nn.avg_pool2d( enhanced_mean, ksize=4, strides=4, padding="VALID" ) d_original_left = tf.nn.conv2d( original_pool, self.left_kernel, strides=[1, 1, 1, 1], padding="SAME" ) d_original_right = tf.nn.conv2d( original_pool, self.right_kernel, strides=[1, 1, 1, 1], padding="SAME" ) d_original_up = tf.nn.conv2d( original_pool, self.up_kernel, strides=[1, 1, 1, 1], padding="SAME" ) d_original_down = tf.nn.conv2d( original_pool, self.down_kernel, strides=[1, 1, 1, 1], padding="SAME" ) d_enhanced_left = tf.nn.conv2d( enhanced_pool, self.left_kernel, strides=[1, 1, 1, 1], padding="SAME" ) d_enhanced_right = tf.nn.conv2d( enhanced_pool, self.right_kernel, strides=[1, 1, 1, 1], padding="SAME" ) d_enhanced_up = tf.nn.conv2d( enhanced_pool, self.up_kernel, strides=[1, 1, 1, 1], padding="SAME" ) d_enhanced_down = tf.nn.conv2d( enhanced_pool, self.down_kernel, strides=[1, 1, 1, 1], padding="SAME" ) d_left = tf.square(d_original_left - d_enhanced_left) d_right = tf.square(d_original_right - d_enhanced_right) d_up = tf.square(d_original_up - d_enhanced_up) d_down = tf.square(d_original_down - d_enhanced_down) return d_left + d_right + d_up + d_down """ ### Deep curve estimation model We implement the Zero-DCE framework as a Keras subclassed model. """ class ZeroDCE(keras.Model): def __init__(self, **kwargs): super().__init__(**kwargs) self.dce_model = build_dce_net() def custom_compile(self, learning_rate, **kwargs): super().compile(**kwargs) self.optimizer = keras.optimizers.Adam(learning_rate=learning_rate) self.spatial_constancy_loss = SpatialConsistencyLoss(reduction="none") def get_enhanced_image(self, data, output): r1 = output[:, :, :, :3] r2 = output[:, :, :, 3:6] r3 = output[:, :, :, 6:9] r4 = output[:, :, :, 9:12] r5 = output[:, :, :, 12:15] r6 = output[:, :, :, 15:18] r7 = output[:, :, :, 18:21] r8 = output[:, :, :, 21:24] x = data + r1 * (tf.square(data) - data) x = x + r2 * (tf.square(x) - x) x = x + r3 * (tf.square(x) - x) enhanced_image = x + r4 * (tf.square(x) - x) x = enhanced_image + r5 * (tf.square(enhanced_image) - enhanced_image) x = x + r6 * (tf.square(x) - x) x = x + r7 * (tf.square(x) - x) enhanced_image = x + r8 * (tf.square(x) - x) return enhanced_image def call(self, data): dce_net_output = self.dce_model(data) return self.get_enhanced_image(data, dce_net_output) def compute_losses(self, data, output): enhanced_image = self.get_enhanced_image(data, output) loss_illumination = 200 * illumination_smoothness_loss(output) loss_spatial_constancy = tf.reduce_mean( self.spatial_constancy_loss(enhanced_image, data) ) loss_color_constancy = 5 * tf.reduce_mean(color_constancy_loss(enhanced_image)) loss_exposure = 10 * tf.reduce_mean(exposure_loss(enhanced_image)) total_loss = ( loss_illumination + loss_spatial_constancy + loss_color_constancy + loss_exposure ) return { "total_loss": total_loss, "illumination_smoothness_loss": loss_illumination, "spatial_constancy_loss": loss_spatial_constancy, "color_constancy_loss": loss_color_constancy, "exposure_loss": loss_exposure, } def train_step(self, data): with tf.GradientTape() as tape: output = self.dce_model(data) losses = self.compute_losses(data, output) gradients = tape.gradient( losses["total_loss"], self.dce_model.trainable_weights ) self.optimizer.apply_gradients(zip(gradients, self.dce_model.trainable_weights)) return losses def test_step(self, data): output = self.dce_model(data) return self.compute_losses(data, output) def save_weights(self, filepath, overwrite=True, save_format=None, options=None): """While saving the weights, we simply save the weights of the DCE-Net""" self.dce_model.save_weights( filepath, overwrite=overwrite, save_format=save_format, options=options ) def load_weights(self, filepath, by_name=False, skip_mismatch=False, options=None): """While loading the weights, we simply load the weights of the DCE-Net""" self.dce_model.load_weights( filepath=filepath, by_name=by_name, skip_mismatch=skip_mismatch, options=options, ) """ ## Training """ zero_dce_model = ZeroDCE() zero_dce_model.custom_compile(learning_rate=1e-4) zero_dce_model.compile(optimizer='adam', loss="mse") history = zero_dce_model.fit(train_dataset, validation_data=val_dataset, epochs=100) def plot_result(item): plt.plot(history.history[item], label=item) plt.plot(history.history["val_" + item], label="val_" + item) plt.xlabel("Epochs") plt.ylabel(item) plt.title("Train and Validation {} Over Epochs".format(item), fontsize=14) plt.legend() plt.grid() plt.show() plot_result("total_loss") plot_result("illumination_smoothness_loss") plot_result("spatial_constancy_loss") plot_result("color_constancy_loss") plot_result("exposure_loss") """ ## Inference """ def plot_results(images, titles, figure_size=(12, 12)): fig = plt.figure(figsize=figure_size) for i in range(len(images)): fig.add_subplot(1, len(images), i + 1).set_title(titles[i]) _ = plt.imshow(images[i]) plt.axis("off") plt.show() def infer(original_image): image = keras.utils.img_to_array(original_image) image = image.astype("float32") / 255.0 image = np.expand_dims(image, axis=0) output_image = zero_dce_model(image) output_image = tf.cast((output_image[0, :, :, :] * 255), dtype=np.uint8) output_image = Image.fromarray(output_image.numpy()) return output_image """ ### Inference on test images We compare the test images from LOLDataset enhanced by MIRNet with images enhanced via the `PIL.ImageOps.autocontrast()` function. You can use the trained model hosted on [Hugging Face Hub](https://huggingface.co/keras-io/low-light-image-enhancement) and try the demo on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/low-light-image-enhancement). """ for val_image_file in test_low_light_images: original_image = Image.open(val_image_file) enhanced_image = infer(original_image) plot_results( [original_image, ImageOps.autocontrast(original_image), enhanced_image], ["Original", "PIL Autocontrast", "Enhanced"], (20, 12), )