HDR Image Reconstruction Using Segmented Image Learning

Abstract

Converting a low dynamic range (LDR) image into a high dynamic range (HDR) image produces an image that closely depicts the real world without requiring expensive devices. Recent deep learning developments can produce highly realistic and sophisticated HDR images. This paper proposes a deep learning method to segment the bright and dark regions from an input LDR image and reconstruct the corresponding HDR image with similar dynamic ranges in the real world. The proposed multi-stage deep learning network brightens bright regions and darkens dark regions, and features with extended brightness range are combined to form the HDR image. Dividing the LDR image into the bright and dark regions effectively implements information on lost over-exposed and under-exposed areas, reconstructing a natural HDR image with color and appearance that is similar to reality. Experimental results confirm that the proposed method achieves an 8.52% higher HDR visual difference predictor (HDR-VDP) and a 41.2% higher log exposure range than current methods. Qualitative evaluation also verifies that the proposed method generates images that are close in quality to the ground truth.

Keywords

• High dynamic range imaging
• inverse tone mapping
• deep learning
• single exposure image