  Light field imaging, which captures spatio-angular information of incident
light on image sensor, enables many interesting applications like image
refocusing and augmented reality. However, due to the limited sensor
resolution, a trade-off exists between the spatial and angular resolution. To
increase the angular resolution, view synthesis techniques have been adopted to
generate new views from existing views. However, traditional learning-based
view synthesis mainly considers the image quality of each view of the light
field and neglects the quality of the refocused images. In this paper, we
propose a new loss function called refocused image error (RIE) to address the
issue. The main idea is that the image quality of the synthesized light field
should be optimized in the refocused image domain because it is where the light
field is perceived. We analyze the behavior of RIL in the spectral domain and
test the performance of our approach against previous approaches on both real
and software-rendered light field datasets using objective assessment metrics
such as MSE, MAE, PSNR, SSIM, and GMSD. Experimental results show that the
light field generated by our method results in better refocused images than
previous methods.
