  Digital imaging aims to replicate realistic scenes, but Low Dynamic Range
(LDR) cameras cannot represent the wide dynamic range of real scenes, resulting
in under-/overexposed images. This paper presents a deep learning-based
approach for recovering intricate details from shadows and highlights while
reconstructing High Dynamic Range (HDR) images. We formulate the problem as an
image-to-image (I2I) translation task and propose a conditional Denoising
Diffusion Probabilistic Model (DDPM) based framework using classifier-free
guidance. We incorporate a deep CNN-based autoencoder in our proposed framework
to enhance the quality of the latent representation of the input LDR image used
for conditioning. Moreover, we introduce a new loss function for LDR-HDR
translation tasks, termed Exposure Loss. This loss helps direct gradients in
the opposite direction of the saturation, further improving the results'
quality. By conducting comprehensive quantitative and qualitative experiments,
we have effectively demonstrated the proficiency of our proposed method. The
results indicate that a simple conditional diffusion-based method can replace
the complex camera pipeline-based architectures.
