Realistically I think you'll often want both depending on what you're doing. Especially for things like blur detection, what's your acceptable specificity, acce

Realistically I think you'll often want both depending on what you're doing. Especially for things like blur detection, what's your acceptable specificity, acceptable/scale of performance, where you're running the algorithm (on device vs cloud). I'm not an expert at all but most image processing network I've seen generally involve at least a few plus a few other layers. I don't think you can get away with a single convolution, at least not that well. OpenCV you could use Laplacian variance which looks like it's just a single line of code. > cv2.Laplacian(image, cv2.CV_64F).var() Many of the NN implementations look like their finetuned off google's ViT checkpoints. I really can't imagine these are faster (at least not without spending extra on GPU/TPU's) than Laplacian variance but I could be wrong. And I assume you might be able to get better evaluation performance from a finetuned NN but depending on what you're doing, that's a ton of work compared to opencv. https://pyimagesearch.com/2015/09/07/blur-detection-with-opencv/ https://pyimagesearch.com/2015/09/07/blur-detection-with-ope... https://sh-tsang.medium.com/review-bdnet-blur-detection-convolutional-neural-network-blur-detection-9150bedd0546 https://sh-tsang.medium.com/review-bdnet-blur-detection-conv...