Noise removal is a fundamental requirement for many image processing applications. Its goal is filtering noise from images while preserving integrity of fine details and edges. In order to assess the quality of image denoising algorithms, it is of paramount importance to use suitable metrics. In other words, the use of an appropriate Image Quality Assessment (IQA) procedure is a key factor for selecting the most suitable image denoising filter and, hence, it has a major impact on the quality of the final image. This paper demonstrates that currently used IQA metrics are not suitable for selecting optimal image denoising filters when edge preservation must be evaluated, because they provide quality figures for the entire image, without specifically addressing edges. From this fact, a method for selecting the optimal filter in this context is proposed, based on minimizing a novel metric that accounts for filter response in edges. Smoothing and sharpening algorithms have been applied to different noisy images and then evaluated using popular metrics and the proposed one. Results show that the new metric is more suitable to assess final image quality when edge deterioration effects have to be accounted for, while offering a simple and understandable approach.