12 megapixel 16-bit linear input (tone-mapped for visualization) tone-mapped with HDR+ 400 -600 ms processed with our algorithm 61 ms, PSNR = 28.4 dB Fig. 1 . Our novel neural network architecture can reproduce sophisticated image enhancements with inference running in real time at full HD resolution on mobile devices. It can not only be used to dramatically accelerate reference implementations, but can also learn subjective effects from human retouching. Performance is a critical challenge in mobile image processing. Given a reference imaging pipeline, or even human-adjusted pairs of images, we seek to reproduce the enhancements and enable real-time evaluation. For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms. Using pairs of input/output images, we train a convolutional neural network to predict the coefficients of a locally-affine model in bilateral space. Our architecture learns to make local, global, and content-dependent decisions to approximate the desired image transformation. At runtime, the neural network consumes a low-resolution version of the input image, produces a set of affine transformations in bilateral space, upsamples those transformations in an edge-preserving fashion using a new slicing node, and then applies those upsampled transformations to the full-resolution image. Our algorithm processes high-resolution images on a smartphone in milliseconds, provides a real-time viewfinder at 1080p resolution, and matches the quality of state-of-the-art approximation techniques on a large class of image operators. Unlike previous work, our model is trained off-line from data and therefore does not require access to the original operator at runtime. This allows our model to learn complex, scene-dependent transformations for which no reference implementation is available, such as the photographic edits of a human retoucher.
Gharbi et al. REF have trained a model to predict local affine transforms in the edge/color bilateral space, which can serve as an approximation to edgeaware image filters and color/tone adjustments for real-time image enhancement.
Deep Bilateral Learning for Real-Time Image Enhancement
ACM Trans. Graph. 36, 4, Article 118 (2017)
Computer Science