Dual-Lens HDR using Guided 3D Exposure CNN and Guided Denoising Transformer

Abstract

We study the high dynamic range (HDR) imaging problem in dual-lens systems. Existing methods usually treat the HDR imaging problem as an image fusion problem and the HDR result is estimated by fusing the aligned short exposure image and long exposure image. However, the image fusion pipeline depends highly on the image alignment, which is difficult to be perfect. We propose to transfer the dual-lens HDR imaging problem into the disentangled enhancement of exposure correction and denoising for the short exposure image, guided by the long exposure image. In the guided exposure correction module, we make use of the guidance image and 3D color transformation to propose a guided 3D exposure CNN (GEC) to get the rough HDR result from the short exposure image. Then, in the guided denoising module, we make use of the cross-attention mechanism to propose a guided denoising transformer (GDT) to directly use the long exposure image as guidance to denoise the rough HDR result in a pyramid way. And in both modules, we bypass the difficult image alignment processing. Experimental results demonstrate the superiority of our method over the state-of-the-art ones.