Rethinking Affine Transform for Efficient Image Enhancement: A Color Space Perspective

In recent years, we have observed significant advancements in learning-based techniques for image-enhancement tasks. However, most of the existing methods are either purely based on image-to-image convolutional neural networks, which cannot handle high-resolution images in real-time, or resort to 3D Lookup Tables, which fall short of local tone adjustments. In this paper, we rethink affine transform through a color space perspective, and then propose AttnBL (Attentional Bilateral Grid Learning), a novel hybrid image enhancement algorithm to process ultra-high-definition images in real-time. Our algorithm consists of two paths, the low-resolution chroma prediction path that aims to learn the chroma coefficients and the full-resolution luma adaptation path that aims to preserve brightness details. Specifically, we propose a carefully designed hierarchical transformer to capture the global information in an efficient way and introduce a feature extraction module to adaptively learn a luma guidance for bilateral upsampling. Our algorithm can process a 4K-resolution image in 20 milliseconds. This efficiency provides a practical solution for high resolution real-time preview. Without bells and whistles, our model outperforms previous state-of-the-art methods on two well-known datasets in image enhancement tasks both quantitatively and qualitatively. Our analysis also provides some interesting findings that may enlighten further studies.