Jiawen Chen

We present a technique for jointly denoising bursts of images taken from a handheld camera. In particular, we propose a convolutional neural network architecture for predicting spatially varying kernels that can both align and denoise frames, a synthetic data generation approach based on a realistic noise formation model, and an optimization guided by an annealed loss function to avoid undesirable local minima. Our model matches or outperforms the state-of-the-art across a wide range of noise levels on both real and synthetic data.

We present a new data structure---the bilateral grid, that enables fast edge-aware image processing. By working in the bilateral grid, algorithms such as bilateral filtering, edge-aware painting, and local histogram equalization become simple manipulations that are both local and independent. We parallelize our algorithms on modern GPUs to achieve real-time frame rates on high-definition video. We demonstrate our method on a variety of applications such as image editing, transfer of photographic look, and contrast enhancement of medical images.

We present a new data structure---the bilateral grid , that enables fast edge-aware image processing. By working in the bilateral grid, algorithms such as bilateral filtering, edge-aware painting, and local histogram equalization become simple manipulations that are both local and independent. We parallelize our algorithms on modern GPUs to achieve real-time frame rates on high-definition video. We demonstrate our method on a variety of applications such as image editing, transfer of photographic look, and contrast enhancement of medical images.

We present an algorithm to accelerate a large class of image processing operators. Given a low-resolution reference input and output pair, we model the operator by fitting local curves that map the input to the output. We can then produce a full-resolution output by evaluating these low-resolution curves on the full-resolution input. We demonstrate that this faithfully models state-of-the-art operators for tone mapping, style transfer, and recoloring. The curves are computed by lifting the input into a bilateral grid and then solving for the 3D array of affine matrices that best maps input color to output color per x, y , intensity bin. We enforce a smoothness term on the matrices which prevents false edges and noise amplification. We can either globally optimize this energy, or quickly approximate a solution by locally fitting matrices and then enforcing smoothness by blurring in grid space. This latter option reduces to joint bilateral upsampling [Kopf et al. 2007] or the guided filter [He et al. 2013], depending on the choice of parameters. The cost of running the algorithm is reduced to the cost of running the original algorithm at greatly reduced resolution, as fitting the curves takes about 10 ms on mobile devices, and 1--2 ms on desktop CPUs, and evaluating the curves can be done with a simple GPU shader.