  Flow-based generative super-resolution (SR) models learn to produce a diverse
set of feasible SR solutions, called the SR space. Diversity of SR solutions
increases with the temperature ($\tau$) of latent variables, which introduces
random variations of texture among sample solutions, resulting in visual
artifacts and low fidelity. In this paper, we present a simple but effective
image ensembling/fusion approach to obtain a single SR image eliminating random
artifacts and improving fidelity without significantly compromising perceptual
quality. We achieve this by benefiting from a diverse set of feasible
photo-realistic solutions in the SR space spanned by flow models. We propose
different image ensembling and fusion strategies which offer multiple paths to
move sample solutions in the SR space to more desired destinations in the
perception-distortion plane in a controllable manner depending on the fidelity
vs. perceptual quality requirements of the task at hand. Experimental results
demonstrate that our image ensembling/fusion strategy achieves more promising
perception-distortion trade-off compared to sample SR images produced by flow
models and adversarially trained models in terms of both quantitative metrics
and visual quality.
