Category: 06. Image enhancement

Introduction Zero-Reference Deep Curve Estimation or Zero-DCE formulates low-light image enhancement as the task of estimating an image-specific tonal curve 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…

Introduction In this example, we implement Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR) by Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. The EDSR architecture is based on the SRResNet architecture and consists of multiple residual blocks. It uses constant scaling layers instead of batch normalization layers to produce consistent results…

Introduction ESPCN (Efficient Sub-Pixel CNN), proposed by Shi, 2016 is a model that reconstructs a high-resolution version of an image given a low-resolution version. It leverages efficient “sub-pixel convolution” layers, which learns an array of image upscaling filters. In this code example, we will implement the model from the paper and train it on a small dataset, BSDS500. BSDS500.…

Introduction With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in photography, security, medical imaging, and remote sensing. In this example, we implement the MIRNet model for low-light image enhancement, a fully-convolutional architecture that learns an enriched set of features that combines contextual information from multiple scales,…

Introduction This example demonstrates how to implement a deep convolutional autoencoder for image denoising, mapping noisy digits images from the MNIST dataset to clean digits images. This implementation is based on an original blog post titled Building Autoencoders in Keras by François Chollet. Setup Prepare the data Build the autoencoder We are going to use the Functional API…