Image Comparative Index (ICI): A Pixel-Wise Image Similarity Metric for Computational Super-Resolution (SR) Microscopy

We compare the outputs of several widely-utilized image quality metrics (such as the PSNR, SSIM, MS-SSIM, FSIMc & IMMSE) in the context of AI-mediated image super-resolution (SR) microscopy derived from a GAN. Although these metrics are often employed in the image analytics space for assaying image quality, the findings of our study indicate that such metrics may not be quite suitable for determining the quality of a GAN-generated image in the context of image SR microscopy, namely in the preservation of fine details and structures which are crucial aspects of high-resolution images (as determined visually). For instance, in some of the assayed images, we observed that these metrics returned a relatively favorable score, while on closer visual inspection, the generated image was observed to be prone to reconstruction artifacts, bearing little similarity to the Expected (ground truth) image. In this respect, we have sought to develop a custom image quality metric capable of assessing image similarity on a pixel-wise scale irrespective of the bit-depth of the image. Our proposed metric [termed the image comparative index (ICI)] has proven to be a viable determinant of similarity between 2 images, closely corroborating with a pixel-wise map of the differences between the assayed images, thereby allowing for more detailed analysis and identification of specific areas of improvement. We postulate that these results represent an important consideration for researchers seeking to utilize deep convolutional neural networks (DCNNs) for image SR (particularly when assessing the similarity between a DCNN-generated and the ground truth images during model training) with potential extrapolations of the proposed ICI metric in image classification & object detection use-cases as well.