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.
Highlights
Substantial image processing advances over the past few years have increased the need for technology to produce images similar to real world resolution
peak signal noise to rate (PSNR) is calculated based on the MSE, which is the Euclidean distance between pixels, with a higher value implying better similarity to the original
High dynamic range (HDR) images should be measured based on human visual perception as well as mathematical differences
Summary
Substantial image processing advances over the past few years have increased the need for technology to produce images similar to real world resolution. HDR images should be obtained with expensive devices that can operate beyond the standard camera sensor limitations to acquire a wide dynamic range [1], and most people have limited access to HDR images. To overcome these limitations, multiple exposure fusion (MEF) methods [2], [3] have emerged, which generate multiexposure LDR images. Inverse tone mapping (ITM) methods [5] – [11] have been proposed to generate HDR images using single LDR images. Previous ITM methods are insufficient for finding a function similar to the inverse camera
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